Add training pipeline (#21)

The pipeline, configurations and datasets may vary from the ones used in the final models [no ci]

training/.gitignore

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+.vscode/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
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+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

training/Engine/evaluate.py

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+import platform
+import torch as T
+import nibabel
+import numpy as np
+
+from monai.networks.nets.unet import UNet
+from monai.data import DataLoader, Dataset
+from monai.metrics import compute_meandice
+from monai.transforms import (
+    AddChanneld,
+    Compose,
+    LoadImaged,
+    NormalizeIntensityd,
+    ToTensord,
+    Resized,
+    AsDiscrete,
+    ThresholdIntensityd,
+    KeepLargestConnectedComponent
+)
+from Engine.utils import (
+    read_yaml,
+    load_model,
+    store_output
+)
+
+def init_eval_file(directory):
+    with open(directory + "/evaluate.csv", "w+") as file:
+        file.write("epoch,train_loss,val_loss,dice_score\n")
+
+def append_eval(eval_path, input_filename, segment_filename, val_loss, dice_score):
+    with open(eval_path, 'a') as file:
+        file.write(f"{input_filename},{segment_filename},{val_loss},{dice_score}\n")
+    
+def evaluate(model, loss_function, loader, device, evaluate_config):
+
+    model.eval()
+    losses = []
+    dices = []
+
+    transform = KeepLargestConnectedComponent([1], connectivity = 3)
+
+
+    with T.no_grad():
+        for image in loader:
+            inp, boxes, label = image["image"].to(device), image["boxes"].to(device), image["label"].to(device)
+            directory_split = '\\' if platform.system() == 'Windows' else '/'
+            output = model(inp)
+        
+            original_filename = image['image_meta_dict']['filename_or_obj'][0]
+            segmentation_filename = f"{evaluate_config['save_directory']}{directory_split}seg_{original_filename.split(directory_split)[-1]}"
+
+            original_image = nibabel.load(original_filename)
+            
+            loss = loss_function(output, label).item()
+            
+            output = output.cpu()
+            output[output >= evaluate_config['output_threshold']] = 1
+            output[output < evaluate_config['output_threshold']] = 0
+            output = output.to(device)
+
+            output = transform(output)
+
+            if(evaluate_config['save_segmentations']):
+                store_output(output, original_image, segmentation_filename, image['image_meta_dict']['affine'].squeeze(0).numpy())
+
+            dice = compute_meandice(output, label).item()
+
+
+            append_eval(evaluate_config['save_directory']+"evaluate.csv", original_filename, segmentation_filename, loss, dice)
+            losses.append(loss)
+            dices.append(dice)
+
+    append_eval(evaluate_config['save_directory']+"evaluate.csv", 'Total', '', sum(losses)/len(losses), sum(dices)/len(dices))
+
+def initiate(config_file):
+    config = read_yaml(config_file)
+    device = T.device(config["device"])
+
+    data = read_yaml(config["data"]["train_dataset"])
+
+    image_shape = (config["data"]["scale_dim"]["d_0"], config["data"]["scale_dim"]["d_1"], config["data"]["scale_dim"]["d_2"])
+
+    for i, d in enumerate(data['data']):
+        data['data'][i]['image'] = data['image_prefix'] + d['label']
+        data['data'][i]['boxes'] = data['boxes_prefix'] + d['label']
+        data['data'][i]['label'] = data['label_prefix'] + d['label']
+
+    transform = Compose(
+        [
+            LoadImaged(keys=["image", "boxes", "label"]),
+            AddChanneld(keys=["image", "boxes", "label"]),
+            ToTensord(keys=["image", "boxes", "label"]),
+        ]
+    )
+
+    dataset = Dataset(data['data'], transform)
+    loader = T.utils.data.DataLoader(dataset, 1)
+
+    model, loss = load_model(config['model'], eval = True)
+    model.load_state_dict(T.load(config["model"]["weights"]))
+    model.to(device)
+
+    init_eval_file(config['evaluate']['save_directory'])
+
+    evaluate(model, loss, loader, device, config['evaluate'])

training/Engine/inference.py

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+import platform
+import nibabel
+import torch as T
+from monai.data import DataLoader, Dataset
+from monai.transforms import (
+    AddChanneld, 
+    Compose, 
+    LoadImaged,
+    NormalizeIntensityd, 
+    Resized,
+    ThresholdIntensityd, 
+    ToTensord
+)
+
+from Engine.utils import (
+    load_model,
+    read_yaml,
+    store_output
+)
+    
+def infer(model, loader, device, infer_config):
+
+    model.eval()
+
+    with T.no_grad():
+        for image in loader:
+            inp, boxes = image["image"].to(device), image["boxes"].to(device)
+            directory_split = '\\' if platform.system() == 'Windows' else '/'
+            output = model(inp)
+        
+            original_filename = image['image_meta_dict']['filename_or_obj'][0]
+            segmentation_filename = f"{infer_config['save_directory']}{directory_split}seg_{original_filename.split(directory_split)[-1]}"
+
+            original_image = nibabel.load(original_filename)
+            upsample = T.nn.Upsample(original_image.shape, mode='trilinear', align_corners = False)
+            output = upsample(output)
+
+            print(image['image_meta_dict']['affine'])
+            store_output(output, original_image, segmentation_filename, image['image_meta_dict']['affine'].squeeze(0).numpy())
+
+
+def initiate(config_file):
+    config = read_yaml(config_file)
+    device = T.device(config["device"])
+
+    data = read_yaml(config["data"]["dataset"])
+
+    image_shape = (config["data"]["scale_dim"]["d_0"], config["data"]["scale_dim"]["d_1"], config["data"]["scale_dim"]["d_2"])
+
+    for i, d in enumerate(data['test']):
+        data['test'][i]['image'] = data['image_prefix'] + d['image']
+        data['test'][i]['boxes'] = data['boxes_prefix'] + d['boxes']
+
+
+    transform = Compose(
+        [
+            LoadImaged(keys=["image", "boxes"]),
+            AddChanneld(keys=["image", "boxes"]),
+            ToTensord(keys=["image", "boxes"]),
+        ]
+    )
+
+    dataset = Dataset(data['data'], transform)
+    loader = T.utils.data.DataLoader(dataset, 1)
+
+    model = load_model(config['model'], infer = True)
+    model.load_state_dict(T.load(config["model"]["weights"]))
+    model.to(device)
+
+    logger.LogInfo("Starting inference!", [str(data)])
+    infer(model, loader, device, config['inference'])
+    logger.LogMilestone("Inference finished!", [])

training/Engine/seg_test.py

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+import torch as T
+import yaml
+import nibabel
+import sys
+from monai.losses import DiceLoss
+
+from monai.networks.nets.unet import UNet
+
+import numpy as np
+import torch as T
+from monai.data import (DataLoader, Dataset)
+from monai.metrics import compute_meandice
+from monai.transforms import (
+    AddChanneld,
+    Compose,
+    LoadImaged,
+    NormalizeIntensityd,
+    ToTensord,
+    Resized,
+    RandFlipd,
+    RandRotate90d,
+    ThresholdIntensityd
+)
+
+from Engine.utils import (
+    load_model,
+    read_yaml
+)
+
+from Networks.UNet4 import UNet4
+
+def init_plot_file(plot_path):
+    with open(plot_path, "w+") as file:
+        file.write("step,loss\n")
+
+def append_metrics(plot_path, epoch, loss):
+    with open(plot_path, 'a') as file:
+        file.write(f"{epoch},{loss}\n")
+
+def main():
+    path = "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_1/full_size.yaml"
+    plot_path = "/cluster/work/sosevle/metrics/t_test3.csv"
+    model_loc = "/cluster/work/sosevle/models/t_test3/"
+    dim = (128,128,128)
+    if sys.platform == "win32":
+        path = "D:\\Repos\\LungTumorSegmentation\\Resources\\Test\\data_test_train.yaml"
+        plot_path = "D:\\Repos\\LungTumorSegmentation\\m_test\\test.csv"
+        model_loc = "D:\\Repos\\LungTumorSegmentation\\m_test\\"
+        dim = (32,32,32)
+
+    data = read_yaml(path)
+
+    for i, d in enumerate(data['train']):
+        data['train'][i]['image'] = data['image_prefix'] + d['image']
+        data['train'][i]['boxes'] = data['boxes_prefix'] + d['boxes']
+        data['train'][i]['label'] = data['label_prefix'] + d['label']
+
+    train_transform = Compose(
+        [
+            LoadImaged(keys=["image", "boxes", "label"]),
+            ThresholdIntensityd(keys=["image"], above=False, threshold=1024, cval=1024),
+            NormalizeIntensityd(keys=["image"], nonzero=True, channel_wise=True),
+            AddChanneld(keys=["image", "boxes", "label"]),
+            Resized(keys=["image", "boxes", "label"], spatial_size=dim),
+            RandFlipd(keys=["image", "boxes", "label"], prob=0.1, spatial_axis=0),
+            RandFlipd(keys=["image", "boxes", "label"], prob=0.1, spatial_axis=1),
+            RandFlipd(keys=["image", "boxes", "label"], prob=0.1, spatial_axis=2),
+            RandRotate90d(keys=["image", "boxes", "label"], prob=0.1, spatial_axes=(0, 1)),
+            ToTensord(keys=["image", "boxes", "label"]),
+        ]
+    )
+
+    train_dataset = Dataset(data['train'], train_transform)
+    train_loader = T.utils.data.DataLoader(train_dataset, 1, shuffle=True)
+
+    device = T.device("cuda")
+
+    #model = T.nn.Sequential(UNet(3, 1, 1, (32, 64, 128, 256), (2, 2, 2)), T.nn.Sigmoid())
+    model = UNet4(32, 2)
+
+    loss_criterion = DiceLoss()
+    optimizer = T.optim.Adam(model.parameters(), 0.0005)
+
+    model.to(device)
+    model.train()
+
+    init_plot_file(plot_path)
+
+    optimizer.zero_grad()
+
+    for i in range(500):
+        print(f"Epoch {i}")
+        epoch_loss = 0
+
+        for j in train_loader:
+            inputs, boxes, labels = j["image"].to(device), j["boxes"].to(device), j["label"].to(device)
+            forwarded = model.forward(inputs)
+            train_loss = loss_criterion(forwarded, labels)
+            train_loss.backward()
+            optimizer.step()
+
+            epoch_loss += train_loss.item()
+            print(f"loss: {train_loss.item()}")
+
+        append_metrics(plot_path, i, epoch_loss / len(train_loader))
+
+        if (i % 10 == 2):
+            T.save(model.state_dict(), model_loc + f"m_{i}.pth")
+
+
+if __name__ == "__main__":
+    main()

training/Engine/train.py

@@ -0,0 +1,201 @@
+import numpy as np
+import torch as T
+from monai.data import (DataLoader, Dataset)
+from monai.metrics import compute_meandice
+from monai.transforms import (
+    AddChanneld,
+    Compose,
+    LoadImaged,
+    NormalizeIntensityd,
+    ToTensord,
+    Resized,
+    RandFlipd,
+    RandRotate90d,
+    ThresholdIntensityd,
+    ScaleIntensityRanged
+)
+
+from Engine.utils import (
+    load_model,
+    read_yaml
+)
+
+
+from Engine.utils import (
+    load_model,
+    read_yaml
+)
+
+from Engine.utils import (
+    load_model,
+    read_yaml
+)
+
+
+def init_plot_file(plot_path):
+    with open(plot_path, "w+") as file:
+        file.write("step,train_loss,val_loss,dice_score\n")
+
+def append_metrics(plot_path, epoch, train_loss, val_loss, dice_score):
+    with open(plot_path, 'a') as file:
+        file.write(f"{epoch},{train_loss},{val_loss},{dice_score}\n")
+
+def train(model, loss_function, optimizer, train_loader, val_loader, device, train_config):
+    best_val_loss = float('inf')
+    val_loss = float('inf')
+    total_steps = 0
+    dcs_metric = 0
+    step_loss = 0
+
+    optimizer.zero_grad()
+
+    for epoch in range(train_config['max_epochs']):
+        print("-" * 10)
+        print(f"epoch {epoch + 1}/{train_config['max_epochs']}")
+        model.train()
+        train_loss = 0
+        step = 0
+        accumulated_loss = 0
+
+        if((epoch + 1) % train_config['save_frequency'] == 0):
+            T.save(model.state_dict(), train_config['model_directory'] + f"model_{epoch + 1}.pth")
+
+        for batch_data in train_loader:
+            total_steps += 1
+            step += 1
+            inputs, boxes, labels = batch_data["image"].to(device), batch_data["boxes"].to(device), batch_data["label"].to(device)
+            loss = model.train_step(inputs, labels, loss_function, boxes)
+            loss.backward()
+            optimizer.step()
+
+            train_loss += loss.detach().item()
+            step_loss += loss.detach().item()
+            
+            print(
+                f"step {step}/{len(train_loader.dataset) // train_loader.batch_size}, "
+                f"train_loss: {loss.item():.6f}")
+            epoch_len = len(train_loader.dataset) // train_loader.batch_size
+            accumulated_loss += loss
+
+            if(total_steps % train_config['batch_size'] == 0):
+
+                accumulated_loss = 0
+                step_loss /= train_config['batch_size']
+                append_metrics(train_config['metric_path'], total_steps, step_loss, val_loss, dcs_metric)
+                step_loss = 0
+
+        train_loss /= len(train_loader.dataset)
+        print(f"epoch {epoch + 1} average loss: {train_loss:.6f}")
+
+        
+
+        if((epoch + 1) % train_config['val_frequency'] == 0):
+            model.eval()
+
+
+            print("Start eval")
+
+            step = 0
+            val_loss = 0
+            dcs_metric = 0
+
+            with T.no_grad():
+                for batch_data in val_loader:
+                    step += 1
+                    inputs, boxes, labels = batch_data["image"].to(device), batch_data["boxes"].to(device), batch_data["label"].to(device)
+                    outputs = model(inputs)
+
+                    loss = loss_function(outputs, labels)
+
+                    outputs = outputs.cpu()
+
+                    outputs[outputs >= train_config['output_threshold']] = 1
+                    outputs[outputs < train_config['output_threshold']] = 0
+                    outputs = outputs.to(device)
+
+                    dcs_metric += T.mean(compute_meandice(outputs.unsqueeze(0), labels.unsqueeze(0))).item()
+                    val_loss += loss.item()
+                    
+            dcs_metric /= len(val_loader.dataset)
+
+                
+            val_loss /= step
+
+            if(val_loss < best_val_loss):
+                best_val_loss = val_loss
+                T.save(model.state_dict(), train_config['model_directory'] + f"model_best.pth")
+
+            print(f"epoch {epoch + 1} validation loss: {val_loss:.6f}")
+            print(f"epoch {epoch + 1} validation dice score: {dcs_metric:.6f}")
+
+        
+        T.save(model.state_dict(), train_config['model_directory'] + f"model_last.pth")
+
+def initiate(config_path):
+    config = read_yaml(config_path)
+    init_plot_file(config['train']['metric_path'])
+    data_paths = config["data"]["train_dataset"]
+    image_shape = (config["data"]["scale_dim"]["d_0"], config["data"]["scale_dim"]["d_1"], config["data"]["scale_dim"]["d_2"])
+
+    combined_train = []
+    combined_val = []
+
+    for data in data_paths:
+        prefixes = read_yaml(data)
+
+        for i, d in enumerate(read_yaml(data)['train']):
+            instance = {
+                'image' : prefixes['image_prefix'] + d['label'],
+                'label' : prefixes['label_prefix'] + d['label'],
+                'boxes' : prefixes['boxes_prefix'] + d['label']
+            }
+            combined_train.append(instance)
+    
+    for data in data_paths:
+        prefixes = read_yaml(data)
+
+        for i, d in enumerate(read_yaml(data)['train']):
+            instance = {
+                'image' : prefixes['image_prefix'] + d['label'],
+                'label' : prefixes['label_prefix'] + d['label'],
+                'boxes' : prefixes['boxes_prefix'] + d['label']
+            }
+            combined_val.append(instance)
+
+    train_transform = Compose(
+        [
+            LoadImaged(keys=["image", "boxes", "label"]),
+            AddChanneld(keys=["image", "boxes", "label"]),
+            RandFlipd(keys=["image", "boxes", "label"], prob=config["data"]["aug_prob"], spatial_axis=0),
+            RandFlipd(keys=["image", "boxes", "label"], prob=config["data"]["aug_prob"], spatial_axis=1),
+            RandFlipd(keys=["image", "boxes", "label"], prob=config["data"]["aug_prob"], spatial_axis=2),
+            RandRotate90d(keys=["image", "boxes", "label"], prob=config["data"]["aug_prob"], spatial_axes=(0, 1)),
+            RandRotate90d(keys=["image", "boxes", "label"], prob=config["data"]["aug_prob"], spatial_axes=(0, 2)),
+            RandRotate90d(keys=["image", "boxes", "label"], prob=config["data"]["aug_prob"], spatial_axes=(1, 2)),
+            ToTensord(keys=["image", "boxes", "label"]),
+        ]
+    )
+
+    val_transform = Compose(
+        [
+            LoadImaged(keys=["image", "boxes", "label"]),
+            AddChanneld(keys=["image", "boxes", "label"]),
+            ToTensord(keys=["image", "boxes", "label"]),
+        ]
+    )
+
+    train_dataset = Dataset(combined_train, train_transform)
+    train_loader = T.utils.data.DataLoader(train_dataset, 1, shuffle = True)
+
+    val_dataset = Dataset(combined_val, val_transform)
+    val_loader = T.utils.data.DataLoader(val_dataset, 1)
+
+    device = T.device(config["device"])
+
+    model, loss, optimizer = load_model(config['model'])
+    if (config["model"]["weights"]):
+        model.load_state_dict(T.load(config["model"]["weights"]))
+    model.to(device)
+
+    print("initiates training!")
+    train(model, loss, optimizer, train_loader, val_loader, device, config['train'])

training/Engine/utils.py

@@ -0,0 +1,79 @@
+import torch as T
+import yaml
+import nibabel
+
+from monai.losses import DiceLoss, GeneralizedDiceLoss, FocalLoss, TverskyLoss
+from Networks.UNet4 import UNet4
+from Networks.UNet5_filter_teacher import UNet5_filter_teacher
+from Networks.UNet5_decoder_teacher import UNet5_decoder_teacher
+from Networks.UNet_test import UNet_student
+from Networks.UNet_concat_double_student import UNet_concat_student
+from monai.networks.nets.unet import UNet
+from Networks.monai_student import UNet_double
+from Networks.monai_unet import UNet_single
+
+
+def load_model(model_config, infer = False, eval = False):
+    model, loss, optim = None, None, None
+    model_name = model_config["architecture"]
+    base = model_config["filter_base"]
+    expansion = model_config["filter_expansion"]
+    
+    filter_layers = model_config["filters"]
+
+    if(model_name == 'UNet_decoder'):
+        model = UNet5_decoder_teacher(base, expansion)
+    elif(model_name == 'UNet_filter'):
+        model = UNet5_filter_teacher(base, expansion)
+    elif(model_name == 'UNet_con_double'):
+        model = UNet_concat_student(base, expansion)
+    elif(model_name == 'UNet_monai'):
+        model = UNet_single(3, 1, 1, tuple(filter_layers), tuple([2 for i in range(len(filter_layers) - 1)]))
+    elif(model_name == 'UNet_monai_double'):
+        model = UNet_double(3, 1, 1, tuple(filter_layers), tuple([2 for i in range(len(filter_layers) - 1)]))
+    elif(model_name == 'UNet4'):
+        model = UNet4(base, expansion)
+    else:
+        print("Architecture not found...")
+        exit(1)
+
+    if(infer):
+        return model
+
+    loss_name = model_config["loss"]
+    if(loss_name == 'BCE'):
+        loss = T.nn.BCELoss()
+    elif(loss_name == 'DiceLoss'):
+        loss = DiceLoss()
+    elif(loss_name == "GenDice"):
+        loss = GeneralizedDiceLoss()
+    elif(loss_name == "Tversky"):
+        loss = TverskyLoss(alpha=2.0, beta=10.0)
+    else:
+        print("Loss not found...")
+        exit(1)
+
+    if(eval):
+        return model, loss
+
+    optim_name = model_config['optimizer']['name']
+    if(optim_name == 'Adam'):
+        optim = T.optim.Adam(model.parameters(), model_config['optimizer']['lr'])
+    else:
+        print("Optimizer not found...")
+        exit(1)
+
+    return model, loss, optim
+
+def read_yaml(yaml_path):
+    with open(yaml_path, 'r') as file:
+        config = yaml.load(file, Loader = yaml.FullLoader)
+    return config
+
+def store_output(output, original_image, directory, affine):
+    
+    headers = original_image.header
+    output = output.squeeze(0).squeeze(0)
+    save = nibabel.Nifti1Image(output.cpu().numpy(), affine, headers)
+
+    nibabel.save(save, directory)

training/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Vemund Fredriksen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

training/Networks/UNet4.py

@@ -0,0 +1,115 @@
+import torch as T
+import torch.nn as nn
+from Networks.network import Network
+
+class UNet4(Network):
+    def __init__(self, base, expansion):
+        super(UNet4, self).__init__()
+        self._build(base, expansion)
+
+    def forward(self, image, segbox=None):
+        layer_0, layer_1, layer_2 = self._analysis(image)
+        return self._synthesis(layer_0, layer_1, layer_2, self._bridge(layer_2))
+
+    def train_step(self, image, segment, criterion, segbox = None):
+        output = self.forward(image)
+
+        loss = criterion(output, segment)
+        return loss
+
+    def _analysis(self, x):
+        layer_0 = self.analysis_0(x)
+        layer_1 = self.analysis_1(layer_0)
+        layer_2 = self.analysis_2(layer_1)
+
+        return layer_0, layer_1, layer_2
+
+    def _bridge(self, layer_2):
+        return self.bridge(layer_2)
+
+    def _synthesis(self, layer_0, layer_1, layer_2, layer_3):
+        concat_2 = T.cat((layer_2, layer_3), dim = 1)
+        concat_1 = T.cat((layer_1, self.synthesis_2(concat_2)), dim = 1)
+        concat_0 = T.cat((layer_0, self.synthesis_1(concat_1)), dim = 1)
+
+        return self.synthesis_0(concat_0)
+
+    def _build(self, base, expansion):
+        layer_0_count = int(base)
+        layer_1_count = int(base * expansion)
+        layer_2_count = int(base * (expansion ** 2))
+        layer_3_count = int(base * (expansion ** 3))
+
+        self.analysis_0 = nn.Sequential(
+            nn.Conv3d(1, layer_0_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_0_count),
+            nn.LeakyReLU(),
+            nn.Conv3d(layer_0_count, layer_0_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_0_count),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_1 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(layer_0_count, layer_1_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_1_count),
+            nn.LeakyReLU(),
+            nn.Conv3d(layer_1_count, layer_1_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_1_count),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_2 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(layer_1_count, layer_2_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_2_count),
+            nn.LeakyReLU(),
+            nn.Conv3d(layer_2_count, layer_2_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_2_count),
+            nn.LeakyReLU(),
+        )
+
+        self.bridge = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(layer_2_count, layer_3_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_3_count),
+            nn.LeakyReLU(),
+            nn.Conv3d(layer_3_count, layer_3_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_3_count),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(layer_3_count, layer_3_count, 2, 2, 0)
+        )
+
+        self.synthesis_2 = nn.Sequential(
+            nn.Conv3d(layer_2_count + layer_3_count, layer_2_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_2_count),
+            nn.LeakyReLU(),
+            nn.Conv3d(layer_2_count,layer_2_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_2_count),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(layer_2_count, layer_2_count, 2, 2, 0)
+        )
+
+        self.synthesis_1 = nn.Sequential(
+            nn.Conv3d(layer_1_count + layer_2_count, layer_1_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_1_count),
+            nn.LeakyReLU(),
+            nn.Conv3d(layer_1_count,layer_1_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_1_count),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(layer_1_count, layer_1_count, 2, 2, 0)
+        )
+
+        self.synthesis_0 = nn.Sequential(
+            nn.Conv3d(layer_0_count + layer_1_count, layer_0_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_0_count),
+            nn.LeakyReLU(),
+            nn.Conv3d(layer_0_count,layer_0_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_0_count),
+            nn.LeakyReLU(),
+            nn.Conv3d(layer_0_count, layer_0_count, 3, 1, 1),
+            nn.BatchNorm3d(layer_0_count),
+            nn.LeakyReLU(),
+            nn.Conv3d(layer_0_count, 1, 3, 1, 1),
+            nn.Sigmoid()
+        )

training/Networks/UNet5_Teacher.py

@@ -0,0 +1,121 @@
+import torch as T
+import torch.nn as nn
+from Networks.network import Network
+
+class UNet5_Teacher(Network):
+    def __init__(self, base, expansion):
+        super(UNet5_Teacher, self).__init__()
+        self._build(base, expansion)
+
+    def forward(self, image, segbox):
+        x_new = T.cat((image, segbox), dim = 1)
+        layer_0, layer_1, layer_2, layer_3 = self._analysis(x_new)
+
+        return self._synthesis(layer_0, layer_1, layer_2, layer_3, self._bridge(layer_3))
+
+    def train_step(self, image, segment, criterion, segbox = None):
+        output = self.forward(image, segbox)
+
+        loss = criterion(output, segment)
+        return loss
+
+    def _analysis(self, x):
+        layer_0 = self.analysis_0(x)
+        layer_1 = self.analysis_1(layer_0)
+        layer_2 = self.analysis_2(layer_1)
+        layer_3 = self.analysis_3(layer_2)
+
+        return layer_0, layer_1, layer_2, layer_3
+
+    def _bridge(self, layer_3):
+        return self.bridge(layer_3)
+
+    def _synthesis(self, l0, l1, l2, l3, l4):
+        c_3 = T.cat((l3, l4), dim = 1)
+        c_2 = T.cat((l2, self.synthesis_3(c_3)), dim = 1)
+        c_1 = T.cat((l1, self.synthesis_2(c_2)), dim = 1)
+        c_0 = T.cat((l0, self.synthesis_1(c_1)), dim = 1)
+
+        return self.synthesis_0(c_0)
+
+    def _build(self, base, expansion):
+        fl_0 = int(base)
+        fl_1 = int(base * expansion)
+        fl_2 = int(base * (expansion ** 2))
+        fl_3 = int(base * (expansion ** 3))
+        fl_4 = int(base * (expansion ** 4))
+
+        self.analysis_0 = nn.Sequential(
+            nn.Conv3d(2, fl_0, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_1 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_0, fl_1, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_1, fl_1, 3, 1, 1),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_2 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_1, fl_2, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_2, fl_2, 3, 1, 1),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_3 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_2, fl_3, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_3, fl_3, 3, 1, 1),
+            nn.LeakyReLU(),
+        )
+
+        self.bridge = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_3, fl_3, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_3, fl_3, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(fl_3, fl_3, 2, 2, 0),
+        )
+
+        self.synthesis_3 = nn.Sequential(
+            nn.Conv3d(fl_3 + fl_3, fl_2, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_2, fl_2, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(fl_2, fl_2, 2, 2, 0),
+        )
+
+        self.synthesis_2 = nn.Sequential(
+            nn.Conv3d(fl_2 + fl_2, fl_1, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_1, fl_1, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(fl_1, fl_1, 2, 2, 0),
+        )
+
+        self.synthesis_1 = nn.Sequential(
+            nn.Conv3d(fl_1 + fl_1, fl_0, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(fl_0, fl_0, 2, 2, 0),
+        )
+
+        self.synthesis_0 = nn.Sequential(
+            nn.Conv3d(fl_0 + fl_0, fl_0, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, 1, 3, 1, 1),
+            nn.Conv3d(1, 1, 1, 1, 0),
+            nn.Sigmoid()
+        )
+    

training/Networks/UNet5_decoder_teacher.py

@@ -0,0 +1,180 @@
+import torch as T
+import torch.nn as nn
+from Networks.network import Network
+
+class UNet5_decoder_teacher(Network):
+    def __init__(self, base, expansion):
+        super(UNet5_decoder_teacher, self).__init__()
+        self._build(base, expansion)
+
+    def forward(self, image, segbox):
+        layer_0, layer_1, layer_2, layer_3 = self._analysis(image)
+        s_0, s_1, s_2, s_3 = self._box_scale(segbox)
+
+        inp_layer_0 = T.cat((layer_0, self.analyse_box_0(s_0)), dim = 1)
+        inp_layer_1 = T.cat((layer_1, self.analyse_box_1(s_1)), dim = 1)
+        inp_layer_2 = T.cat((layer_2, self.analyse_box_2(s_2)), dim = 1)
+        inp_layer_3 = T.cat((layer_3, self.analyse_box_3(s_3)), dim = 1)
+
+        return self._synthesis(inp_layer_0, inp_layer_1, inp_layer_2, inp_layer_3, self._bridge(layer_3))
+
+    def train_step(self, image, segment, criterion, segbox = None):
+        output = self.forward(image, segbox)
+
+        loss = criterion(output, segment)
+        return loss
+
+    def _analysis(self, x):
+        layer_0 = self.analysis_0(x)
+        layer_1 = self.analysis_1(layer_0)
+        layer_2 = self.analysis_2(layer_1)
+        layer_3 = self.analysis_3(layer_2)
+
+        return layer_0, layer_1, layer_2, layer_3
+
+    def _bridge(self, layer_3):
+        return self.bridge(layer_3)
+
+    def _synthesis(self, l0, l1, l2, l3, l4):
+        c_3 = T.cat((l3, l4), dim = 1)
+        c_2 = T.cat((l2, self.synthesis_3(c_3)), dim = 1)
+        c_1 = T.cat((l1, self.synthesis_2(c_2)), dim = 1)
+        c_0 = T.cat((l0, self.synthesis_1(c_1)), dim = 1)
+
+        return self.synthesis_0(c_0)
+
+    def _box_scale(self, b):
+        s_0 = b
+        s_1 = nn.functional.interpolate(s_0, scale_factor = (0.5, 0.5, 0.5))
+        s_2 = nn.functional.interpolate(s_1, scale_factor = (0.5, 0.5, 0.5))
+        s_3 = nn.functional.interpolate(s_2, scale_factor = (0.5, 0.5, 0.5))
+        return s_0, s_1, s_2, s_3
+
+    def _build(self, base, expansion):
+        fl_0 = int(base)
+        fl_1 = int(base * expansion)
+        fl_2 = int(base * (expansion ** 2))
+        fl_3 = int(base * (expansion ** 3))
+
+        self.analyse_box_0 = nn.Sequential(
+            nn.Conv3d(1, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.Sigmoid(),
+        )
+
+        self.analyse_box_1 = nn.Sequential(
+            nn.Conv3d(1, 128, 3, 1, 1),
+            nn.BatchNorm3d(128),
+            nn.Sigmoid(),
+        )
+
+        self.analyse_box_2 = nn.Sequential(
+            nn.Conv3d(1, 256, 3, 1, 1),
+            nn.BatchNorm3d(256),
+            nn.Sigmoid(),
+        )
+
+        self.analyse_box_3 = nn.Sequential(
+            nn.Conv3d(1, 512, 3, 1, 1),
+            nn.BatchNorm3d(512),
+            nn.Sigmoid(),
+        )
+
+        self.analysis_0 = nn.Sequential(
+            nn.Conv3d(1, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+            nn.Conv3d(64, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_1 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(64, 128, 3, 1, 1),
+            nn.BatchNorm3d(128),
+            nn.LeakyReLU(),
+            nn.Conv3d(128, 128, 3, 1, 1),
+            nn.BatchNorm3d(128),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_2 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(128, 256, 3, 1, 1),
+            nn.BatchNorm3d(256),
+            nn.LeakyReLU(),
+            nn.Conv3d(256, 256, 3, 1, 1),
+            nn.BatchNorm3d(256),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_3 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(256, 512, 3, 1, 1),
+            nn.BatchNorm3d(512),
+            nn.LeakyReLU(),
+            nn.Conv3d(512, 512, 3, 1, 1),
+            nn.BatchNorm3d(512),
+            nn.LeakyReLU(),
+        )
+
+        self.bridge = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(512, 512, 3, 1, 1),
+            nn.BatchNorm3d(512),
+            nn.LeakyReLU(),
+            nn.Conv3d(512, 512, 3, 1, 1),
+            nn.BatchNorm3d(512),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(512, 512, 2, 2, 0),
+        )
+
+        self.synthesis_3 = nn.Sequential(
+            nn.Conv3d(512 + 512 + 512, 256, 3, 1, 1),
+            nn.BatchNorm3d(256),
+            nn.LeakyReLU(),
+            nn.Conv3d(256, 256, 3, 1, 1),
+            nn.BatchNorm3d(256),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(256, 256, 2, 2, 0),
+        )
+
+        self.synthesis_2 = nn.Sequential(
+            nn.Conv3d(256 + 256 + 256, 128, 3, 1, 1),
+            nn.BatchNorm3d(128),
+            nn.LeakyReLU(),
+            nn.Conv3d(128, 128, 3, 1, 1),
+            nn.BatchNorm3d(128),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(128, 128, 2, 2, 0),
+        )
+
+        self.synthesis_1 = nn.Sequential(
+            nn.Conv3d(128 + 128 + 128, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+            nn.Conv3d(64, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(64, 64, 2, 2, 0),
+        )
+
+        self.synthesis_0 = nn.Sequential(
+            nn.Conv3d(64 + 64 + 64, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+            nn.Conv3d(64, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+            nn.Conv3d(64, 1, 3, 1, 1),
+            nn.Sigmoid()
+        )
+
+if __name__ == "__main__":
+    s = UNet_teacher()
+    f = T.randn((1,1,64,64,64))
+    a = s._box_scale(f)
+
+    print(a[3].shape)
+    

training/Networks/UNet5_filter_teacher.py

@@ -0,0 +1,137 @@
+import torch as T
+import torch.nn as nn
+from Networks.network import Network
+
+class UNet5_filter_teacher(Network):
+    def __init__(self, base, expansion):
+        super(UNet5_filter_teacher, self).__init__()
+        self._build(base, expansion)
+
+    def forward(self, image, segbox):
+        x_new = T.cat((image, segbox), dim = 1)
+        layer_0, layer_1, layer_2, layer_3 = self._analysis(x_new)
+
+        return self._synthesis(layer_0, layer_1, layer_2, layer_3, self._bridge(layer_3))
+
+    def train_step(self, image, segment, criterion, segbox = None):
+        output = self.forward(image, segbox)
+
+        loss = criterion(output, segment)
+        return loss
+
+    def _analysis(self, x):
+        layer_0 = self.analysis_0(x)
+        layer_1 = self.analysis_1(layer_0)
+        layer_2 = self.analysis_2(layer_1)
+        layer_3 = self.analysis_3(layer_2)
+
+        return layer_0, layer_1, layer_2, layer_3
+
+    def _bridge(self, layer_3):
+        return self.bridge(layer_3)
+
+    def _synthesis(self, l0, l1, l2, l3, l4):
+        c_3 = T.cat((l3, l4), dim = 1)
+        c_2 = T.cat((l2, self.synthesis_3(c_3)), dim = 1)
+        c_1 = T.cat((l1, self.synthesis_2(c_2)), dim = 1)
+        c_0 = T.cat((l0, self.synthesis_1(c_1)), dim = 1)
+
+        return self.synthesis_0(c_0)
+
+    def _build(self, base, expansion):
+        fl_0 = int(base)
+        fl_1 = int(base * expansion)
+        fl_2 = int(base * (expansion ** 2))
+        fl_3 = int(base * (expansion ** 3))
+
+        self.analysis_0 = nn.Sequential(
+            nn.Conv3d(2, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_1 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_0, fl_1, 3, 1, 1),
+            nn.BatchNorm3d(fl_1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_1, fl_1, 3, 1, 1),
+            nn.BatchNorm3d(fl_1),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_2 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_1, fl_2, 3, 1, 1),
+            nn.BatchNorm3d(fl_2),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_2, fl_2, 3, 1, 1),
+            nn.BatchNorm3d(fl_2),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_3 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_2, fl_3, 3, 1, 1),
+            nn.BatchNorm3d(fl_3),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_3, fl_3, 3, 1, 1),
+            nn.BatchNorm3d(fl_3),
+            nn.LeakyReLU(),
+        )
+
+        self.bridge = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_3, fl_3, 3, 1, 1),
+            nn.BatchNorm3d(fl_3),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_3, fl_3, 3, 1, 1),
+            nn.BatchNorm3d(fl_3),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(fl_3, fl_3, 2, 2, 0),
+        )
+
+        self.synthesis_3 = nn.Sequential(
+            nn.Conv3d(fl_3 + fl_3, fl_2, 3, 1, 1),
+            nn.BatchNorm3d(fl_2),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_2, fl_2, 3, 1, 1),
+            nn.BatchNorm3d(fl_2),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(fl_2, fl_2, 2, 2, 0),
+        )
+
+        self.synthesis_2 = nn.Sequential(
+            nn.Conv3d(fl_2 + fl_2, fl_1, 3, 1, 1),
+            nn.BatchNorm3d(fl_1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_1, fl_1, 3, 1, 1),
+            nn.BatchNorm3d(fl_1),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(fl_1, fl_1, 2, 2, 0),
+        )
+
+        self.synthesis_1 = nn.Sequential(
+            nn.Conv3d(fl_1 + fl_1, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(fl_0, fl_0, 2, 2, 0),
+        )
+
+        self.synthesis_0 = nn.Sequential(
+            nn.Conv3d(fl_0 + fl_0, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, 1, 3, 1, 1),
+            nn.Sigmoid()
+        )
+    

training/Networks/UNet_concat_double_student.py

@@ -0,0 +1,198 @@
+import torch as T
+import torch.nn as nn
+from Networks.network import Network
+
+class UNet_concat_student(Network):
+    def __init__(self, base, expansion):
+        super(UNet_concat_student, self).__init__()
+        self._build(base, expansion)
+
+    def forward(self, image, segbox = None,  eval_mode = True):
+        layer_0, layer_1, layer_2, layer_3 = self._analysis(image)
+
+        inter_output, s_3, s_2, s_1 = self._synthesis(layer_0, layer_1, layer_2, layer_3, self._bridge(layer_3))
+        output_0 = self.output_0(inter_output)
+
+        u_0 = self._upsample(s_3, s_2, s_1, inter_output)
+        output_1 = self.output_1(u_0)
+
+        if (eval_mode):
+            return output_0
+        return output_0, output_1
+
+    def train_step(self, image, segment, criterion, segbox = None):
+        seg_out, box_out = self.forward(image, eval_mode=False)
+
+        seg_loss = criterion(seg_out, segment)
+        box_loss = criterion(box_out, segbox)
+        return seg_loss + box_loss
+
+    def _analysis(self, x):
+        layer_0 = self.analysis_0(x)
+        layer_1 = self.analysis_1(layer_0)
+        layer_2 = self.analysis_2(layer_1)
+        layer_3 = self.analysis_3(layer_2)
+
+        return layer_0, layer_1, layer_2, layer_3
+
+    def _bridge(self, layer_3):
+        return self.bridge(layer_3)
+
+    def _synthesis(self, l0, l1, l2, l3, l4):
+        c_3 = T.cat((l3, l4), dim = 1)
+        s_3 = self.synthesis_3(c_3)
+        c_2 = T.cat((l2, self.up_conv_3(s_3)), dim = 1)
+        s_2 = self.synthesis_2(c_2)
+        c_1 = T.cat((l1, self.up_conv_2(s_2)), dim = 1)
+        s_1 = self.synthesis_1(c_1)
+        c_0 = T.cat((l0, self.up_conv_1(s_1)), dim = 1)
+
+        return self.synthesis_0(c_0), s_3, s_2, s_1
+
+    def _upsample(self, s_3, s_2, s_1, inter):
+        u_3 = self.up_sample_3(s_3)
+        u_2 = self.up_sample_2(s_2)
+        u_1 = self.up_sample_1(s_1)
+
+        return T.cat((u_3, u_2, u_1, inter), dim = 1)
+
+    def _build(self, base, expansion):
+        fl_i1 = int(base / (expansion ** 2))
+        fl_i0 = int(base / expansion)
+        fl_0 = int(base)
+        fl_1 = int(base * expansion)
+        fl_2 = int(base * (expansion ** 2))
+        fl_3 = int(base * (expansion ** 3))
+
+        self.analysis_0 = nn.Sequential(
+            nn.Conv3d(1, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_1 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_0, fl_1, 3, 1, 1),
+            nn.BatchNorm3d(fl_1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_1, fl_1, 3, 1, 1),
+            nn.BatchNorm3d(fl_1),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_2 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_1, fl_2, 3, 1, 1),
+            nn.BatchNorm3d(fl_2),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_2, fl_2, 3, 1, 1),
+            nn.BatchNorm3d(fl_2),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_3 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_2, fl_3, 3, 1, 1),
+            nn.BatchNorm3d(fl_3),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_3, fl_3, 3, 1, 1),
+            nn.BatchNorm3d(fl_3),
+            nn.LeakyReLU(),
+        )
+
+        self.bridge = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(fl_3, fl_3, 3, 1, 1),
+            nn.BatchNorm3d(fl_3),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_3, fl_3, 3, 1, 1),
+            nn.BatchNorm3d(fl_3),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(fl_3, fl_3, 2, 2, 0),
+        )
+
+        self.synthesis_3 = nn.Sequential(
+            nn.Conv3d(fl_3 + fl_3, fl_2, 3, 1, 1),
+            nn.BatchNorm3d(fl_2),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_2, fl_2, 3, 1, 1),
+            nn.BatchNorm3d(fl_2),
+            nn.LeakyReLU(),
+        )
+
+        self.synthesis_2 = nn.Sequential(
+            nn.Conv3d(fl_2 + fl_2, fl_1, 3, 1, 1),
+            nn.BatchNorm3d(fl_1),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_1, fl_1, 3, 1, 1),
+            nn.BatchNorm3d(fl_1),
+            nn.LeakyReLU(),
+        )
+
+        self.synthesis_1 = nn.Sequential(
+            nn.Conv3d(fl_1 + fl_1, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+        )
+
+        self.up_conv_3 = nn.Sequential(
+            nn.ConvTranspose3d(fl_2, fl_2, 2, 2, 0),
+        )
+
+        self.up_conv_2 = nn.Sequential(
+            nn.ConvTranspose3d(fl_1, fl_1, 2, 2, 0),
+        )
+
+        self.up_conv_1 = nn.Sequential(
+            nn.ConvTranspose3d(fl_0, fl_0, 2, 2, 0),
+        )
+
+        self.synthesis_0 = nn.Sequential(
+            nn.Conv3d(fl_0 + fl_0, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+        )
+
+        self.output_0 = nn.Sequential(
+            nn.Conv3d(fl_0, fl_i0, 3, 1, 1),
+            nn.BatchNorm3d(fl_i0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_i0, 1, 3, 1, 1),
+            nn.Sigmoid()
+        )
+
+        self.up_sample_3 = nn.Sequential(
+            nn.ConvTranspose3d(fl_2, fl_0, 8, 8, 0),
+        )
+
+        self.up_sample_2 = nn.Sequential(
+            nn.ConvTranspose3d(fl_1, fl_i0, 4, 4, 0),
+        )
+
+        self.up_sample_1 = nn.Sequential(
+            nn.ConvTranspose3d(fl_0, fl_i1, 2, 2, 0),
+        )
+
+        self.output_1 = nn.Sequential(
+            nn.Conv3d(fl_0 + fl_0 + fl_i0 + fl_i1, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_0, 3, 1, 1),
+            nn.BatchNorm3d(fl_0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_0, fl_i0, 3, 1, 1),
+            nn.BatchNorm3d(fl_i0),
+            nn.LeakyReLU(),
+            nn.Conv3d(fl_i0, 1, 3, 1, 1),
+            nn.Sigmoid()
+        )
+    

training/Networks/UNet_test.py

@@ -0,0 +1,205 @@
+import torch as T
+import torch.nn as nn
+from Networks.network import Network
+from monai.networks.nets.unet import UNet
+
+class UNet_student(Network):
+    def __init__(self):
+        super(UNet_student, self).__init__()
+        #self._build()
+        self.model = T.nn.Sequential(UNet(3, 1, 1, (32, 64, 128, 256), (2, 2, 2)), T.nn.Sigmoid())
+
+    def forward(self, image):
+        return self.model(image)
+    
+    def train_step(self, image, segment, criterion, segbox):
+        fo = self.forward(image)
+        loss = criterion(image, segment)
+        return loss
+
+
+    """
+    def forward(self, image, segbox = None, eval_mode = True):
+
+
+        layer_0, layer_1, layer_2, layer_3 = self._analysis(image)
+
+        inter_output, s_3, s_2, s_1 = self._synthesis(layer_0, layer_1, layer_2, layer_3, self._bridge(layer_3))
+        seg_out = self.output_0(inter_output)
+
+        u_0 = self._upsample(s_3, s_2, s_1, inter_output)
+        box_out = self.output_1(u_0)
+
+        if (eval_mode):
+            return seg_out
+        return seg_out, box_out
+
+    def train_step(self, image, segment, criterion, segbox = None):
+        seg_out, box_out = self.forward(image, eval_mode=False)
+
+        seg_loss = criterion(seg_out, segment)
+        box_loss = criterion(box_out, segbox)
+        return seg_loss + box_loss
+
+    def _analysis(self, x):
+        layer_0 = self.analysis_0(x)
+        layer_1 = self.analysis_1(layer_0)
+        layer_2 = self.analysis_2(layer_1)
+        layer_3 = self.analysis_3(layer_2)
+
+        return layer_0, layer_1, layer_2, layer_3
+
+    def _bridge(self, layer_3):
+        return self.bridge(layer_3)
+
+    def _synthesis(self, l0, l1, l2, l3, l4):
+        c_3 = T.cat((l3, l4), dim = 1)
+        s_3 = self.synthesis_3(c_3)
+        c_2 = T.cat((l2, self.up_conv_3(s_3)), dim = 1)
+        s_2 = self.synthesis_2(c_2)
+        c_1 = T.cat((l1, self.up_conv_2(s_2)), dim = 1)
+        s_1 = self.synthesis_1(c_1)
+        c_0 = T.cat((l0, self.up_conv_1(s_1)), dim = 1)
+
+        return self.synthesis_0(c_0), s_3, s_2, s_1
+
+    def _upsample(self, s_3, s_2, s_1, inter):
+        u_3 = self.up_sample_3(s_3)
+        u_2 = self.up_sample_2(s_2)
+        u_1 = self.up_sample_1(s_1)
+
+        return T.cat((u_3, u_2, u_1, inter), dim = 1)
+
+    def _build(self):
+        self.analysis_0 = nn.Sequential(
+            nn.Conv3d(1, 16, 3, 1, 1),
+            nn.BatchNorm3d(16),
+            nn.LeakyReLU(),
+            nn.Conv3d(16, 24, 3, 1, 1),
+            nn.BatchNorm3d(24),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_1 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(24, 40, 3, 1, 1),
+            nn.BatchNorm3d(40),
+            nn.LeakyReLU(),
+            nn.Conv3d(40, 48, 3, 1, 1),
+            nn.BatchNorm3d(48),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_2 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(48, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+            nn.Conv3d(64, 86, 3, 1, 1),
+            nn.BatchNorm3d(86),
+            nn.LeakyReLU(),
+        )
+
+        self.analysis_3 = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(86, 110, 3, 1, 1),
+            nn.BatchNorm3d(110),
+            nn.LeakyReLU(),
+            nn.Conv3d(110, 128, 3, 1, 1),
+            nn.BatchNorm3d(128),
+            nn.LeakyReLU(),
+        )
+
+        self.bridge = nn.Sequential(
+            nn.MaxPool3d(2),
+            nn.Conv3d(128, 128, 3, 1, 1),
+            nn.BatchNorm3d(128),
+            nn.LeakyReLU(),
+            nn.Conv3d(128, 128, 3, 1, 1),
+            nn.BatchNorm3d(128),
+            nn.LeakyReLU(),
+            nn.ConvTranspose3d(128, 128, 2, 2, 0),
+        )
+
+        self.synthesis_3 = nn.Sequential(
+            nn.Conv3d(128 + 128, 110, 3, 1, 1),
+            nn.BatchNorm3d(110),
+            nn.LeakyReLU(),
+            nn.Conv3d(110, 86, 3, 1, 1),
+            nn.BatchNorm3d(86),
+            nn.LeakyReLU(),
+        )
+
+        self.synthesis_2 = nn.Sequential(
+            nn.Conv3d(86 + 86, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+            nn.Conv3d(64, 48, 3, 1, 1),
+            nn.BatchNorm3d(48),
+            nn.LeakyReLU(),
+        )
+
+        self.synthesis_1 = nn.Sequential(
+            nn.Conv3d(48 + 48, 40, 3, 1, 1),
+            nn.BatchNorm3d(40),
+            nn.LeakyReLU(),
+            nn.Conv3d(40, 24, 3, 1, 1),
+            nn.BatchNorm3d(24),
+            nn.LeakyReLU(),
+        )
+
+        self.up_conv_3 = nn.Sequential(
+            nn.ConvTranspose3d(86, 86, 2, 2, 0),
+        )
+
+        self.up_conv_2 = nn.Sequential(
+            nn.ConvTranspose3d(48, 48, 2, 2, 0),
+        )
+
+        self.up_conv_1 = nn.Sequential(
+            nn.ConvTranspose3d(24, 24, 2, 2, 0),
+        )
+
+        self.synthesis_0 = nn.Sequential(
+            nn.Conv3d(24 + 24, 16, 3, 1, 1),
+            nn.BatchNorm3d(16),
+            nn.LeakyReLU(),
+            nn.Conv3d(16, 16, 3, 1, 1),
+            nn.BatchNorm3d(16),
+            nn.LeakyReLU(),
+        )
+
+        self.output_0 = nn.Sequential(
+            nn.Conv3d(16, 8, 3, 1, 1),
+            nn.BatchNorm3d(8),
+            nn.LeakyReLU(),
+            nn.Conv3d(8, 1, 3, 1, 1),
+            nn.Sigmoid()
+        )
+
+        self.up_sample_3 = nn.Sequential(
+            nn.ConvTranspose3d(86, 32, 8, 8, 0),
+        )
+
+        self.up_sample_2 = nn.Sequential(
+            nn.ConvTranspose3d(48, 16, 4, 4, 0),
+        )
+
+        self.up_sample_1 = nn.Sequential(
+            nn.ConvTranspose3d(24, 8, 2, 2, 0),
+        )
+
+        self.output_1 = nn.Sequential(
+            nn.Conv3d(72, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+            nn.Conv3d(64, 64, 3, 1, 1),
+            nn.BatchNorm3d(64),
+            nn.LeakyReLU(),
+            nn.Conv3d(64, 32, 3, 1, 1),
+            nn.BatchNorm3d(32),
+            nn.LeakyReLU(),
+            nn.Conv3d(32, 1, 3, 1, 1),
+            nn.Sigmoid()
+        )
+    """

training/Networks/monai_student.py

@@ -0,0 +1,162 @@
+from typing import Sequence, Union
+
+import torch
+import torch.nn as nn
+
+from monai.networks.blocks.convolutions import Convolution, ResidualUnit
+from monai.networks.layers.factories import Act, Norm
+from monai.networks.layers.simplelayers import SkipConnection
+from monai.utils import alias, export
+
+class UNet_double(nn.Module):
+    def __init__(
+        self,
+        dimensions: int,
+        in_channels: int,
+        out_channels: int,
+        channels: Sequence[int],
+        strides: Sequence[int],
+        kernel_size: Union[Sequence[int], int] = 3,
+        up_kernel_size: Union[Sequence[int], int] = 3,
+        num_res_units: int = 0,
+        act=Act.PRELU,
+        norm=Norm.INSTANCE,
+        dropout=0.0,) -> None:
+
+        super().__init__()
+
+        self.dimensions = dimensions
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.channels = channels
+        self.strides = strides
+        self.kernel_size = kernel_size
+        self.up_kernel_size = up_kernel_size
+        self.num_res_units = num_res_units
+        self.act = act
+        self.norm = norm
+        self.dropout = dropout
+
+        def _create_block(
+            inc: int, outc: int, channels: Sequence[int], strides: Sequence[int], is_top: bool) -> nn.Sequential:
+            
+            c = channels[0]
+            s = strides[0]
+
+            subblock: nn.Module
+
+            if len(channels) > 2:
+                subblock1, subblock2 = _create_block(c, c, channels[1:], strides[1:], False)  # continue recursion down
+                upc = c * 2
+            else:
+                # the next layer is the bottom so stop recursion, create the bottom layer as the sublock for this layer
+                subblock = self._get_bottom_layer(c, channels[1])
+                upc = c + channels[1]
+
+                down = self._get_down_layer(inc, c, s, is_top)  # create layer in downsampling path
+                up1 = self._get_up_layer(upc, outc, s, is_top)  # create layer in upsampling path
+                up2 = self._get_up_layer(upc, outc, s, is_top)
+
+                return nn.Sequential(down, SkipConnection(subblock), up1), nn.Sequential(down, SkipConnection(subblock), up2)
+
+            down = self._get_down_layer(inc, c, s, is_top)  # create layer in downsampling path
+            up1 = self._get_up_layer(upc, outc, s, is_top)  # create layer in upsampling path
+            up2 = self._get_up_layer(upc, outc, s, is_top)
+
+            return nn.Sequential(down, SkipConnection(subblock1), up1), nn.Sequential(down, SkipConnection(subblock2), up2)
+
+        self.model1, self.model2 = _create_block(in_channels, out_channels, self.channels, self.strides, True)
+        self.activation = nn.Sigmoid()
+
+    def _get_down_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:
+        """
+        Args:
+            in_channels: number of input channels.
+            out_channels: number of output channels.
+            strides: convolution stride.
+            is_top: True if this is the top block.
+        """
+        if self.num_res_units > 0:
+            return ResidualUnit(
+                self.dimensions,
+                in_channels,
+                out_channels,
+                strides=strides,
+                kernel_size=self.kernel_size,
+                subunits=self.num_res_units,
+                act=self.act,
+                norm=self.norm,
+                dropout=self.dropout,
+            )
+        return Convolution(
+            self.dimensions,
+            in_channels,
+            out_channels,
+            strides=strides,
+            kernel_size=self.kernel_size,
+            act=self.act,
+            norm=self.norm,
+            dropout=self.dropout,
+        )
+
+    def _get_bottom_layer(self, in_channels: int, out_channels: int) -> nn.Module:
+        """
+        Args:
+            in_channels: number of input channels.
+            out_channels: number of output channels.
+        """
+        return self._get_down_layer(in_channels, out_channels, 1, False)
+
+    def _get_up_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:
+        """
+        Args:
+            in_channels: number of input channels.
+            out_channels: number of output channels.
+            strides: convolution stride.
+            is_top: True if this is the top block.
+        """
+        conv: Union[Convolution, nn.Sequential]
+
+        conv = Convolution(
+            self.dimensions,
+            in_channels,
+            out_channels,
+            strides=strides,
+            kernel_size=self.up_kernel_size,
+            act=self.act,
+            norm=self.norm,
+            dropout=self.dropout,
+            conv_only=is_top and self.num_res_units == 0,
+            is_transposed=True,
+        )
+
+        if self.num_res_units > 0:
+            ru = ResidualUnit(
+                self.dimensions,
+                out_channels,
+                out_channels,
+                strides=1,
+                kernel_size=self.kernel_size,
+                subunits=1,
+                act=self.act,
+                norm=self.norm,
+                dropout=self.dropout,
+                last_conv_only=is_top,
+            )
+            conv = nn.Sequential(conv, ru)
+
+        return conv
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.activation(self.model1(x))
+
+    def train_step(self, image, segment, criterion, segbox = None):
+        forwarded = self.forward_train(image)
+        target = torch.cat((segment, segbox), dim = 1)
+        return criterion(forwarded, target)
+
+    def forward_train(self, image):
+        x1 = self.model1(image)
+        x2 = self.model2(image)
+        return self.activation(torch.cat((x1, x2), dim = 1))
+

training/Networks/monai_unet.py

@@ -0,0 +1,160 @@
+from typing import Sequence, Union
+
+import torch
+import torch.nn as nn
+
+from monai.networks.blocks.convolutions import Convolution, ResidualUnit
+from monai.networks.layers.factories import Act, Norm
+from monai.networks.layers.simplelayers import SkipConnection
+from monai.utils import alias, export
+
+class UNet_single(nn.Module):
+    def __init__(
+        self,
+        dimensions: int,
+        in_channels: int,
+        out_channels: int,
+        channels: Sequence[int],
+        strides: Sequence[int],
+        kernel_size: Union[Sequence[int], int] = 3,
+        up_kernel_size: Union[Sequence[int], int] = 3,
+        num_res_units: int = 0,
+        act=Act.PRELU,
+        norm=Norm.INSTANCE,
+        dropout=0.0,) -> None:
+
+        super().__init__()
+
+        self.dimensions = dimensions
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.channels = channels
+        self.strides = strides
+        self.kernel_size = kernel_size
+        self.up_kernel_size = up_kernel_size
+        self.num_res_units = num_res_units
+        self.act = act
+        self.norm = norm
+        self.dropout = dropout
+
+        def _create_block(
+            inc: int, outc: int, channels: Sequence[int], strides: Sequence[int], is_top: bool) -> nn.Sequential:
+            """
+            Builds the UNet structure from the bottom up by recursing down to the bottom block, then creating sequential
+            blocks containing the downsample path, a skip connection around the previous block, and the upsample path.
+
+            Args:
+                inc: number of input channels.
+                outc: number of output channels.
+                channels: sequence of channels. Top block first.
+                strides: convolution stride.
+                is_top: True if this is the top block.
+            """
+            c = channels[0]
+            s = strides[0]
+
+            subblock: nn.Module
+
+            if len(channels) > 2:
+                subblock = _create_block(c, c, channels[1:], strides[1:], False)  # continue recursion down
+                upc = c * 2
+            else:
+                # the next layer is the bottom so stop recursion, create the bottom layer as the sublock for this layer
+                subblock = self._get_bottom_layer(c, channels[1])
+                upc = c + channels[1]
+
+            down = self._get_down_layer(inc, c, s, is_top)  # create layer in downsampling path
+            up = self._get_up_layer(upc, outc, s, is_top)  # create layer in upsampling path
+
+            return nn.Sequential(down, SkipConnection(subblock), up)
+
+        self.model = _create_block(in_channels, out_channels, self.channels, self.strides, True)
+        self.activation = nn.Sigmoid()
+
+    def _get_down_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:
+        """
+        Args:
+            in_channels: number of input channels.
+            out_channels: number of output channels.
+            strides: convolution stride.
+            is_top: True if this is the top block.
+        """
+        if self.num_res_units > 0:
+            return ResidualUnit(
+                self.dimensions,
+                in_channels,
+                out_channels,
+                strides=strides,
+                kernel_size=self.kernel_size,
+                subunits=self.num_res_units,
+                act=self.act,
+                norm=self.norm,
+                dropout=self.dropout,
+            )
+        return Convolution(
+            self.dimensions,
+            in_channels,
+            out_channels,
+            strides=strides,
+            kernel_size=self.kernel_size,
+            act=self.act,
+            norm=self.norm,
+            dropout=self.dropout,
+        )
+
+    def _get_bottom_layer(self, in_channels: int, out_channels: int) -> nn.Module:
+        """
+        Args:
+            in_channels: number of input channels.
+            out_channels: number of output channels.
+        """
+        return self._get_down_layer(in_channels, out_channels, 1, False)
+
+    def _get_up_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:
+        """
+        Args:
+            in_channels: number of input channels.
+            out_channels: number of output channels.
+            strides: convolution stride.
+            is_top: True if this is the top block.
+        """
+        conv: Union[Convolution, nn.Sequential]
+
+        conv = Convolution(
+            self.dimensions,
+            in_channels,
+            out_channels,
+            strides=strides,
+            kernel_size=self.up_kernel_size,
+            act=self.act,
+            norm=self.norm,
+            dropout=self.dropout,
+            conv_only=is_top and self.num_res_units == 0,
+            is_transposed=True,
+        )
+
+        if self.num_res_units > 0:
+            ru = ResidualUnit(
+                self.dimensions,
+                out_channels,
+                out_channels,
+                strides=1,
+                kernel_size=self.kernel_size,
+                subunits=1,
+                act=self.act,
+                norm=self.norm,
+                dropout=self.dropout,
+                last_conv_only=is_top,
+            )
+            conv = nn.Sequential(conv, ru)
+
+        return conv
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.activation(self.model(x))
+
+    def train_step(self, image, segment, criterion, segbox = None):
+        forwarded = self.forward(image)
+        return criterion(forwarded, segment)
+
+

training/Networks/network.py

@@ -0,0 +1,11 @@
+import torch as T
+
+class Network(T.nn.Module):
+    def __init__(self):
+        super(Network, self).__init__()
+
+    def forward(self, image, segbox):
+        pass
+
+    def train_step(self, image, segment, criterion, segbox):
+        pass

training/README.md

@@ -0,0 +1,101 @@
+# Lung Tumor 3D Segmentation - Mixed Supervision
+
+End to end code base for lung tumor segmentation from CT-scan using mixed supervision for deep convolutional neural network. Takes 3D CT scans as input and outputs 3D segmentation of primary tumor. Project aims to utilize multiple different datasets with different label types (classification labels, bounding boxes, rough segmentations and fine segmentations).
+
+## Installation
+
+Requires python >= 3.6
+
+Install python dependencies with pip. The requirements file is located in */Resources/*
+```
+pip install -r requirements.txt
+```
+
+Build repository files as packages using setuptools. If you alter the code, remember to run the setup again.
+```
+python setupy.py install
+```
+
+## Docker Integration
+*To be specified*
+
+## Usage
+
+**Initiate Training:**  `python .\Runable\Test\test_sevlus.py`
+
+The training is initiated from the .\Resources\Test\test_train.yaml file that looks like this:
+```
+device: 'cuda'
+
+data:
+  train_dataset: "D:\\Repos\\LungTumorSegmentation\\Resources\\Test\\data_test_train.yaml"
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 32
+    d_1: 32
+    d_2: 32
+
+model:
+  architecture: UNet_con_double
+  loss: DiceLoss
+  filter_base: 64
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.001
+
+train:
+  max_epochs: 3
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "D:\\Repos\\LungTumorSegmentation\\metrics.csv"
+  save_frequency: 5
+  model_directory: "D:\\Repos\\LungTumorSegmentation\\models\\"
+  steps_per_plot: 10
+```
+
+The paths would need to be updated.
+
+**Plot Training Metric File**: `python interface.py plot <path_to_metric_file>`
+
+Store the plot by specifying the store location: `python interface.py plot <path_to_metric_file> -store_file <path_to_store> -steps_per_epoch <steps_per_epoch (int)>` Example: `python interface.py plot ./metrics.csv -store_file plot.png -steps_per_epoch 120`
+
+**Evaluate Network**: `python .\Runable\Evaluate\eval.py`
+
+Evaluates the network on specified test images with labels. The configuration file (which here is the file at .\Resources\Windows_Files\win_eval.yaml) should contain network information as well as a reference to the data to evaluate the network on.
+
+The evaluation config should look similar to this
+
+```
+device: 'cuda'
+
+data:
+  train_dataset: "D:\\Repos\\LungTumorSegmentation\\Resources\\Windows_Files\\evaluate_data_msd.yaml"
+  scale_dim:
+    d_0: 128
+    d_1: 128
+    d_2: 128
+
+model:
+  architecture: UNet_filter
+  weights: "D:\\idun_models\\1\\model_last_128.pth"
+  loss: DiceLoss
+  filter_base: 128
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.01
+
+evaluate:
+  save_segmentations: True
+  save_directory: 'D:\\Repos\\LungTumorSegmentation\\m_128\\'
+  output_threshold: 0.5
+```
+
+In this case, the paths would also need to be updated.
+
+## Authors
+[Vemund Fredrksen](https://github.com/VemundFredriksen) & [Svein Ole M. Sevle](https://github.com/sosevle)
+
+## License
+[MIT](https://choosealicense.com/licenses/mit/)

training/Resources/Evaluate/malvik.yaml

@@ -0,0 +1,23 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/home/tumor/LungTumorSegmentation/Resources/Inference/malvik_inference_data2.yaml"
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet_test
+  weights: ""
+  loss: DiceLoss
+  filter_base: 32
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.01
+
+evaluate:
+  save_segmentations: True
+  save_directory: ''
+  output_threshold: 0.5

training/Resources/Idun_Train_1/cropped.yaml

@@ -0,0 +1,320 @@
+image_prefix: "/cluster/work/sosevle/MSD_Cropped/Images/"
+boxes_prefix: "/cluster/work/sosevle/MSD_Cropped/Boxes/"
+label_prefix: "/cluster/work/sosevle/MSD_Cropped/Labels/"
+
+train:
+  [
+    {
+      'image' : 'lung_001.nii',
+      'boxes' : 'lung_001.nii',
+      'label' : 'lung_001.nii'
+    },
+    {
+      'image' : 'lung_003.nii',
+      'boxes' : 'lung_003.nii',
+      'label' : 'lung_003.nii'
+    },
+    {
+      'image' : 'lung_004.nii',
+      'boxes' : 'lung_004.nii',
+      'label' : 'lung_004.nii'
+    },
+    {
+      'image' : 'lung_005.nii',
+      'boxes' : 'lung_005.nii',
+      'label' : 'lung_005.nii'
+    },
+    {
+      'image' : 'lung_006.nii',
+      'boxes' : 'lung_006.nii',
+      'label' : 'lung_006.nii'
+    },
+    {
+      'image' : 'lung_009.nii',
+      'boxes' : 'lung_009.nii',
+      'label' : 'lung_009.nii'
+    },
+    {
+      'image' : 'lung_010.nii',
+      'boxes' : 'lung_010.nii',
+      'label' : 'lung_010.nii'
+    },
+    {
+      'image' : 'lung_014.nii',
+      'boxes' : 'lung_014.nii',
+      'label' : 'lung_014.nii'
+    },
+    {
+      'image' : 'lung_015.nii',
+      'boxes' : 'lung_015.nii',
+      'label' : 'lung_015.nii'
+    },
+    {
+      'image' : 'lung_016.nii',
+      'boxes' : 'lung_016.nii',
+      'label' : 'lung_016.nii'
+    },
+    {
+      'image' : 'lung_018.nii',
+      'boxes' : 'lung_018.nii',
+      'label' : 'lung_018.nii'
+    },
+    {
+      'image' : 'lung_020.nii',
+      'boxes' : 'lung_020.nii',
+      'label' : 'lung_020.nii'
+    },
+    {
+      'image' : 'lung_022.nii',
+      'boxes' : 'lung_022.nii',
+      'label' : 'lung_022.nii'
+    },
+    {
+      'image' : 'lung_023.nii',
+      'boxes' : 'lung_023.nii',
+      'label' : 'lung_023.nii'
+    },
+    {
+      'image' : 'lung_025.nii',
+      'boxes' : 'lung_025.nii',
+      'label' : 'lung_025.nii'
+    },
+    {
+      'image' : 'lung_026.nii',
+      'boxes' : 'lung_026.nii',
+      'label' : 'lung_026.nii'
+    },
+    {
+      'image' : 'lung_027.nii',
+      'boxes' : 'lung_027.nii',
+      'label' : 'lung_027.nii'
+    },
+    {
+      'image' : 'lung_028.nii',
+      'boxes' : 'lung_028.nii',
+      'label' : 'lung_028.nii'
+    },
+    {
+      'image' : 'lung_029.nii',
+      'boxes' : 'lung_029.nii',
+      'label' : 'lung_029.nii'
+    },
+    {
+      'image' : 'lung_031.nii',
+      'boxes' : 'lung_031.nii',
+      'label' : 'lung_031.nii'
+    },
+    {
+      'image' : 'lung_033.nii',
+      'boxes' : 'lung_033.nii',
+      'label' : 'lung_033.nii'
+    },
+    {
+      'image' : 'lung_034.nii',
+      'boxes' : 'lung_034.nii',
+      'label' : 'lung_034.nii'
+    },
+    {
+      'image' : 'lung_036.nii',
+      'boxes' : 'lung_036.nii',
+      'label' : 'lung_036.nii'
+    },
+    {
+      'image' : 'lung_037.nii',
+      'boxes' : 'lung_037.nii',
+      'label' : 'lung_037.nii'
+    },
+    {
+      'image' : 'lung_038.nii',
+      'boxes' : 'lung_038.nii',
+      'label' : 'lung_038.nii'
+    },
+    {
+      'image' : 'lung_041.nii',
+      'boxes' : 'lung_041.nii',
+      'label' : 'lung_041.nii'
+    },
+    {
+      'image' : 'lung_042.nii',
+      'boxes' : 'lung_042.nii',
+      'label' : 'lung_042.nii'
+    },
+    {
+      'image' : 'lung_043.nii',
+      'boxes' : 'lung_043.nii',
+      'label' : 'lung_043.nii'
+    },
+    {
+      'image' : 'lung_044.nii',
+      'boxes' : 'lung_044.nii',
+      'label' : 'lung_044.nii'
+    },
+    {
+      'image' : 'lung_045.nii',
+      'boxes' : 'lung_045.nii',
+      'label' : 'lung_045.nii'
+    },
+    {
+      'image' : 'lung_046.nii',
+      'boxes' : 'lung_046.nii',
+      'label' : 'lung_046.nii'
+    },
+    {
+      'image' : 'lung_047.nii',
+      'boxes' : 'lung_047.nii',
+      'label' : 'lung_047.nii'
+    },
+    {
+      'image' : 'lung_048.nii',
+      'boxes' : 'lung_048.nii',
+      'label' : 'lung_048.nii'
+    },
+    {
+      'image' : 'lung_049.nii',
+      'boxes' : 'lung_049.nii',
+      'label' : 'lung_049.nii'
+    },
+    {
+      'image' : 'lung_051.nii',
+      'boxes' : 'lung_051.nii',
+      'label' : 'lung_051.nii'
+    },
+    {
+      'image' : 'lung_053.nii',
+      'boxes' : 'lung_053.nii',
+      'label' : 'lung_053.nii'
+    },
+    {
+      'image' : 'lung_054.nii',
+      'boxes' : 'lung_054.nii',
+      'label' : 'lung_054.nii'
+    },
+    {
+      'image' : 'lung_055.nii',
+      'boxes' : 'lung_055.nii',
+      'label' : 'lung_055.nii'
+    },
+    {
+      'image' : 'lung_057.nii',
+      'boxes' : 'lung_057.nii',
+      'label' : 'lung_057.nii'
+    },
+    {
+      'image' : 'lung_058.nii',
+      'boxes' : 'lung_058.nii',
+      'label' : 'lung_058.nii'
+    },
+    {
+      'image' : 'lung_059.nii',
+      'boxes' : 'lung_059.nii',
+      'label' : 'lung_059.nii'
+    },
+    {
+      'image' : 'lung_061.nii',
+      'boxes' : 'lung_061.nii',
+      'label' : 'lung_061.nii'
+    },
+    {
+      'image' : 'lung_062.nii',
+      'boxes' : 'lung_062.nii',
+      'label' : 'lung_062.nii'
+    },
+    {
+      'image' : 'lung_064.nii',
+      'boxes' : 'lung_064.nii',
+      'label' : 'lung_064.nii'
+    },
+    {
+      'image' : 'lung_065.nii',
+      'boxes' : 'lung_065.nii',
+      'label' : 'lung_065.nii'
+    },
+    {
+      'image' : 'lung_066.nii',
+      'boxes' : 'lung_066.nii',
+      'label' : 'lung_066.nii'
+    },
+    {
+      'image' : 'lung_069.nii',
+      'boxes' : 'lung_069.nii',
+      'label' : 'lung_069.nii'
+    },
+    {
+      'image' : 'lung_070.nii',
+      'boxes' : 'lung_070.nii',
+      'label' : 'lung_070.nii'
+    },
+    {
+      'image' : 'lung_071.nii',
+      'boxes' : 'lung_071.nii',
+      'label' : 'lung_071.nii'
+    },
+    {
+      'image' : 'lung_073.nii',
+      'boxes' : 'lung_073.nii',
+      'label' : 'lung_073.nii'
+    },
+    {
+      'image' : 'lung_074.nii',
+      'boxes' : 'lung_074.nii',
+      'label' : 'lung_074.nii'
+    },
+    {
+      'image' : 'lung_075.nii',
+      'boxes' : 'lung_075.nii',
+      'label' : 'lung_075.nii'
+    },
+    {
+      'image' : 'lung_078.nii',
+      'boxes' : 'lung_078.nii',
+      'label' : 'lung_078.nii'
+    },
+    {
+      'image' : 'lung_079.nii',
+      'boxes' : 'lung_079.nii',
+      'label' : 'lung_079.nii'
+    },
+    {
+      'image' : 'lung_080.nii',
+      'boxes' : 'lung_080.nii',
+      'label' : 'lung_080.nii'
+    }
+]
+
+val: [
+    {
+      'image' : 'lung_081.nii',
+      'boxes' : 'lung_081.nii',
+      'label' : 'lung_081.nii'
+    },
+    {
+      'image' : 'lung_083.nii',
+      'boxes' : 'lung_083.nii',
+      'label' : 'lung_083.nii'
+    },
+    {
+      'image' : 'lung_084.nii',
+      'boxes' : 'lung_084.nii',
+      'label' : 'lung_084.nii'
+    },
+    {
+      'image' : 'lung_086.nii',
+      'boxes' : 'lung_086.nii',
+      'label' : 'lung_086.nii'
+    },
+    {
+      'image' : 'lung_092.nii',
+      'boxes' : 'lung_092.nii',
+      'label' : 'lung_092.nii'
+    },
+    {
+      'image' : 'lung_093.nii',
+      'boxes' : 'lung_093.nii',
+      'label' : 'lung_093.nii'
+    },
+    {
+      'image' : 'lung_095.nii',
+      'boxes' : 'lung_095.nii',
+      'label' : 'lung_095.nii'
+    }
+]

training/Resources/Idun_Train_1/full_size.yaml

@@ -0,0 +1,320 @@
+image_prefix: "/cluster/work/sosevle/d1/imagesTr/"
+boxes_prefix: "/cluster/work/sosevle/d1/boxes/"
+label_prefix: "/cluster/work/sosevle/d1/labelsTr/"
+
+train:
+  [
+    {
+      'image' : 'lung_001.nii/lung_001.nii',
+      'boxes' : 'lung_001.nii',
+      'label': 'lung_001.nii/lung_001.nii'
+    },
+    {
+      'image' : 'lung_003.nii/lung_003.nii',
+      'boxes' : 'lung_003.nii',
+      'label': 'lung_003.nii/lung_003.nii'
+    },
+    {
+      'image' : 'lung_004.nii/lung_004.nii',
+      'boxes' : 'lung_004.nii',
+      'label': 'lung_004.nii/lung_004.nii'
+    },
+    {
+      'image' : 'lung_005.nii/lung_005.nii',
+      'boxes' : 'lung_005.nii',
+      'label': 'lung_005.nii/lung_005.nii'
+    },
+    {
+      'image' : 'lung_006.nii/lung_006.nii',
+      'boxes' : 'lung_006.nii',
+      'label': 'lung_006.nii/lung_006.nii'
+    },
+    {
+      'image' : 'lung_009.nii/lung_009.nii',
+      'boxes' : 'lung_009.nii',
+      'label': 'lung_009.nii/lung_009.nii'
+    },
+    {
+      'image' : 'lung_010.nii/lung_010.nii',
+      'boxes' : 'lung_010.nii',
+      'label': 'lung_010.nii/lung_010.nii'
+    },
+    {
+      'image' : 'lung_014.nii/lung_014.nii',
+      'boxes' : 'lung_014.nii',
+      'label': 'lung_014.nii/lung_014.nii'
+    },
+    {
+      'image' : 'lung_015.nii/lung_015.nii',
+      'boxes' : 'lung_015.nii',
+      'label': 'lung_015.nii/lung_015.nii'
+    },
+    {
+      'image' : 'lung_016.nii/lung_016.nii',
+      'boxes' : 'lung_016.nii',
+      'label': 'lung_016.nii/lung_016.nii'
+    },
+    {
+      'image' : 'lung_018.nii/lung_018.nii',
+      'boxes' : 'lung_018.nii',
+      'label': 'lung_018.nii/lung_018.nii'
+    },
+    {
+      'image' : 'lung_020.nii/lung_020.nii',
+      'boxes' : 'lung_020.nii',
+      'label': 'lung_020.nii/lung_020.nii'
+    },
+    {
+      'image' : 'lung_022.nii/lung_022.nii',
+      'boxes' : 'lung_022.nii',
+      'label': 'lung_022.nii/lung_022.nii'
+    },
+    {
+      'image' : 'lung_023.nii/lung_023.nii',
+      'boxes' : 'lung_023.nii',
+      'label': 'lung_023.nii/lung_023.nii'
+    },
+    {
+      'image' : 'lung_025.nii/lung_025.nii',
+      'boxes' : 'lung_025.nii',
+      'label': 'lung_025.nii/lung_025.nii'
+    },
+    {
+      'image' : 'lung_026.nii/lung_026.nii',
+      'boxes' : 'lung_026.nii',
+      'label': 'lung_026.nii/lung_026.nii'
+    },
+    {
+      'image' : 'lung_027.nii/lung_027.nii',
+      'boxes' : 'lung_027.nii',
+      'label': 'lung_027.nii/lung_027.nii'
+    },
+    {
+      'image' : 'lung_028.nii/lung_028.nii',
+      'boxes' : 'lung_028.nii',
+      'label': 'lung_028.nii/lung_028.nii'
+    },
+    {
+      'image' : 'lung_029.nii/lung_029.nii',
+      'boxes' : 'lung_029.nii',
+      'label': 'lung_029.nii/lung_029.nii'
+    },
+    {
+      'image' : 'lung_031.nii/lung_031.nii',
+      'boxes' : 'lung_031.nii',
+      'label': 'lung_031.nii/lung_031.nii'
+    },
+    {
+      'image' : 'lung_033.nii/lung_033.nii',
+      'boxes' : 'lung_033.nii',
+      'label': 'lung_033.nii/lung_033.nii'
+    },
+    {
+      'image' : 'lung_034.nii/lung_034.nii',
+      'boxes' : 'lung_034.nii',
+      'label': 'lung_034.nii/lung_034.nii'
+    },
+    {
+      'image' : 'lung_036.nii/lung_036.nii',
+      'boxes' : 'lung_036.nii',
+      'label': 'lung_036.nii/lung_036.nii'
+    },
+    {
+      'image' : 'lung_037.nii/lung_037.nii',
+      'boxes' : 'lung_037.nii',
+      'label': 'lung_037.nii/lung_037.nii'
+    },
+    {
+      'image' : 'lung_038.nii/lung_038.nii',
+      'boxes' : 'lung_038.nii',
+      'label': 'lung_038.nii/lung_038.nii'
+    },
+    {
+      'image' : 'lung_041.nii/lung_041.nii',
+      'boxes' : 'lung_041.nii',
+      'label': 'lung_041.nii/lung_041.nii'
+    },
+    {
+      'image' : 'lung_042.nii/lung_042.nii',
+      'boxes' : 'lung_042.nii',
+      'label': 'lung_042.nii/lung_042.nii'
+    },
+    {
+      'image' : 'lung_043.nii/lung_043.nii',
+      'boxes' : 'lung_043.nii',
+      'label': 'lung_043.nii/lung_043.nii'
+    },
+    {
+      'image' : 'lung_044.nii/lung_044.nii',
+      'boxes' : 'lung_044.nii',
+      'label': 'lung_044.nii/lung_044.nii'
+    },
+    {
+      'image' : 'lung_045.nii/lung_045.nii',
+      'boxes' : 'lung_045.nii',
+      'label': 'lung_045.nii/lung_045.nii'
+    },
+    {
+      'image' : 'lung_046.nii/lung_046.nii',
+      'boxes' : 'lung_046.nii',
+      'label': 'lung_046.nii/lung_046.nii'
+    },
+    {
+      'image' : 'lung_047.nii/lung_047.nii',
+      'boxes' : 'lung_047.nii',
+      'label': 'lung_047.nii/lung_047.nii'
+    },
+    {
+      'image' : 'lung_048.nii/lung_048.nii',
+      'boxes' : 'lung_048.nii',
+      'label': 'lung_048.nii/lung_048.nii'
+    },
+    {
+      'image' : 'lung_049.nii/lung_049.nii',
+      'boxes' : 'lung_049.nii',
+      'label': 'lung_049.nii/lung_049.nii'
+    },
+    {
+      'image' : 'lung_051.nii/lung_051.nii',
+      'boxes' : 'lung_051.nii',
+      'label': 'lung_051.nii/lung_051.nii'
+    },
+    {
+      'image' : 'lung_053.nii/lung_053.nii',
+      'boxes' : 'lung_053.nii',
+      'label': 'lung_053.nii/lung_053.nii'
+    },
+    {
+      'image' : 'lung_054.nii/lung_054.nii',
+      'boxes' : 'lung_054.nii',
+      'label': 'lung_054.nii/lung_054.nii'
+    },
+    {
+      'image' : 'lung_055.nii/lung_055.nii',
+      'boxes' : 'lung_055.nii',
+      'label': 'lung_055.nii/lung_055.nii'
+    },
+    {
+      'image' : 'lung_057.nii/lung_057.nii',
+      'boxes' : 'lung_057.nii',
+      'label': 'lung_057.nii/lung_057.nii'
+    },
+    {
+      'image' : 'lung_058.nii/lung_058.nii',
+      'boxes' : 'lung_058.nii',
+      'label': 'lung_058.nii/lung_058.nii'
+    },
+    {
+      'image' : 'lung_059.nii/lung_059.nii',
+      'boxes' : 'lung_059.nii',
+      'label': 'lung_059.nii/lung_059.nii'
+    },
+    {
+      'image' : 'lung_061.nii/lung_061.nii',
+      'boxes' : 'lung_061.nii',
+      'label': 'lung_061.nii/lung_061.nii'
+    },
+    {
+      'image' : 'lung_062.nii/lung_062.nii',
+      'boxes' : 'lung_062.nii',
+      'label': 'lung_062.nii/lung_062.nii'
+    },
+    {
+      'image' : 'lung_064.nii/lung_064.nii',
+      'boxes' : 'lung_064.nii',
+      'label': 'lung_064.nii/lung_064.nii'
+    },
+    {
+      'image' : 'lung_065.nii/lung_065.nii',
+      'boxes' : 'lung_065.nii',
+      'label': 'lung_065.nii/lung_065.nii'
+    },
+    {
+      'image' : 'lung_066.nii/lung_066.nii',
+      'boxes' : 'lung_066.nii',
+      'label': 'lung_066.nii/lung_066.nii'
+    },
+    {
+      'image' : 'lung_069.nii/lung_069.nii',
+      'boxes' : 'lung_069.nii',
+      'label': 'lung_069.nii/lung_069.nii'
+    },
+    {
+      'image' : 'lung_070.nii/lung_070.nii',
+      'boxes' : 'lung_070.nii',
+      'label': 'lung_070.nii/lung_070.nii'
+    },
+    {
+      'image' : 'lung_071.nii/lung_071.nii',
+      'boxes' : 'lung_071.nii',
+      'label': 'lung_071.nii/lung_071.nii'
+    },
+    {
+      'image' : 'lung_073.nii/lung_073.nii',
+      'boxes' : 'lung_073.nii',
+      'label': 'lung_073.nii/lung_073.nii'
+    },
+    {
+      'image' : 'lung_074.nii/lung_074.nii',
+      'boxes' : 'lung_074.nii',
+      'label': 'lung_074.nii/lung_074.nii'
+    },
+    {
+      'image' : 'lung_075.nii/lung_075.nii',
+      'boxes' : 'lung_075.nii',
+      'label': 'lung_075.nii/lung_075.nii'
+    },
+    {
+      'image' : 'lung_078.nii/lung_078.nii',
+      'boxes' : 'lung_078.nii',
+      'label': 'lung_078.nii/lung_078.nii'
+    },
+    {
+      'image' : 'lung_079.nii/lung_079.nii',
+      'boxes' : 'lung_079.nii',
+      'label': 'lung_079.nii/lung_079.nii'
+    },
+    {
+      'image' : 'lung_080.nii/lung_080.nii',
+      'boxes' : 'lung_080.nii',
+      'label': 'lung_080.nii/lung_080.nii'
+    }
+]
+
+val: [
+    {
+      'image' : 'lung_081.nii/lung_081.nii',
+      'boxes' : 'lung_081.nii',
+      'label': 'lung_081.nii/lung_081.nii'
+    },
+    {
+      'image' : 'lung_083.nii/lung_083.nii',
+      'boxes' : 'lung_083.nii',
+      'label': 'lung_083.nii/lung_083.nii'
+    },
+    {
+      'image' : 'lung_084.nii/lung_084.nii',
+      'boxes' : 'lung_084.nii',
+      'label': 'lung_084.nii/lung_084.nii'
+    },
+    {
+      'image' : 'lung_086.nii/lung_086.nii',
+      'boxes' : 'lung_086.nii',
+      'label': 'lung_086.nii/lung_086.nii'
+    },
+    {
+      'image' : 'lung_092.nii/lung_092.nii',
+      'boxes' : 'lung_092.nii',
+      'label': 'lung_092.nii/lung_092.nii'
+    },
+    {
+      'image' : 'lung_093.nii/lung_093.nii',
+      'boxes' : 'lung_093.nii',
+      'label': 'lung_093.nii/lung_093.nii'
+    },
+    {
+      'image' : 'lung_095.nii/lung_095.nii',
+      'boxes' : 'lung_095.nii',
+      'label': 'lung_095.nii/lung_095.nii'
+    }
+]

training/Resources/Idun_Train_1/train_25.yaml

@@ -0,0 +1,27 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_1/cropped.yaml"
+  aug_prob: 0.25
+  scale_dim:
+    d_0: 128
+    d_1: 128
+    d_2: 128
+
+model:
+  architecture: UNet_filter
+  loss: DiceLoss
+  filter_base: 64
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.0002
+
+train:
+  max_epochs: 500
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t25.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/25/"
+  steps_per_plot: 10

training/Resources/Idun_Train_1/train_50.yaml

@@ -0,0 +1,28 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_1/cropped.yaml"
+  aug_prob: 0.5
+  scale_dim:
+    d_0: 128
+    d_1: 128
+    d_2: 128
+
+model:
+  architecture: UNet_filter
+  loss: DiceLoss
+  weights: /cluster/work/sosevle/models/50/model_500.pth
+  filter_base: 64
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.0002
+
+train:
+  max_epochs: 500
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t50_2.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/50_2/"
+  batch_size: 10

training/Resources/Idun_Train_1/train_75.yaml

@@ -0,0 +1,27 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_1/cropped.yaml"
+  aug_prob: 0.75
+  scale_dim:
+    d_0: 128
+    d_1: 128
+    d_2: 128
+
+model:
+  architecture: UNet_filter
+  loss: DiceLoss
+  filter_base: 64
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.0002
+
+train:
+  max_epochs: 500
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t75.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/75/"
+  steps_per_plot: 10

training/Resources/Idun_Train_1/train_full_filter_cropped.yaml

@@ -0,0 +1,27 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_1/cropped.yaml"
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 128
+    d_1: 128
+    d_2: 128
+
+model:
+  architecture: UNet_filter
+  loss: DiceLoss
+  filter_base: 64
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.0002
+
+train:
+  max_epochs: 500
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/full_filter.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/full_filter/"
+  steps_per_plot: 10

training/Resources/Idun_Train_1/train_full_filter_full_image.yaml

@@ -0,0 +1,27 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_1/full_size.yaml"
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 128
+    d_1: 128
+    d_2: 128
+
+model:
+  architecture: UNet_filter
+  loss: DiceLoss
+  filter_base: 64
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.0002
+
+train:
+  max_epochs: 500
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/full_filter_fs.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/full_filter_fs/"
+  steps_per_plot: 10

training/Resources/Idun_Train_1/train_half_filter_cropped.yaml

@@ -0,0 +1,27 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_1/cropped.yaml"
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 128
+    d_1: 128
+    d_2: 128
+
+model:
+  architecture: UNet_filter
+  loss: DiceLoss
+  filter_base: 32
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.0002
+
+train:
+  max_epochs: 500
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/half_filter.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/half_filter/"
+  steps_per_plot: 10

training/Resources/Idun_Train_2/data.yaml

@@ -0,0 +1,3123 @@
+image_prefix: "/cluster/work/sosevle/full_data/train_student/Images/"
+label_prefix: "/cluster/work/sosevle/full_data/train_student/Labels/"
+boxes_prefix: "/cluster/work/sosevle/full_data/train_student/Boxes/"
+
+train:
+  [
+    {
+      'image' : 'lung_001.nii.gz',
+      'boxes' : 'lung_001.nii.gz',
+      'label': 'lung_001.nii.gz',
+    },
+    {
+      'image' : 'lung_003.nii.gz',
+      'boxes' : 'lung_003.nii.gz',
+      'label': 'lung_003.nii.gz',
+    },
+    {
+      'image' : 'lung_004.nii.gz',
+      'boxes' : 'lung_004.nii.gz',
+      'label': 'lung_004.nii.gz',
+    },
+    {
+      'image' : 'lung_005.nii.gz',
+      'boxes' : 'lung_005.nii.gz',
+      'label': 'lung_005.nii.gz',
+    },
+    {
+      'image' : 'lung_006.nii.gz',
+      'boxes' : 'lung_006.nii.gz',
+      'label': 'lung_006.nii.gz',
+    },
+    {
+      'image' : 'lung_009.nii.gz',
+      'boxes' : 'lung_009.nii.gz',
+      'label': 'lung_009.nii.gz',
+    },
+    {
+      'image' : 'lung_010.nii.gz',
+      'boxes' : 'lung_010.nii.gz',
+      'label': 'lung_010.nii.gz',
+    },
+    {
+      'image' : 'lung_014.nii.gz',
+      'boxes' : 'lung_014.nii.gz',
+      'label': 'lung_014.nii.gz',
+    },
+    {
+      'image' : 'lung_015.nii.gz',
+      'boxes' : 'lung_015.nii.gz',
+      'label': 'lung_015.nii.gz',
+    },
+    {
+      'image' : 'lung_016.nii.gz',
+      'boxes' : 'lung_016.nii.gz',
+      'label': 'lung_016.nii.gz',
+    },
+    {
+      'image' : 'lung_018.nii.gz',
+      'boxes' : 'lung_018.nii.gz',
+      'label': 'lung_018.nii.gz',
+    },
+    {
+      'image' : 'lung_020.nii.gz',
+      'boxes' : 'lung_020.nii.gz',
+      'label': 'lung_020.nii.gz',
+    },
+    {
+      'image' : 'lung_022.nii.gz',
+      'boxes' : 'lung_022.nii.gz',
+      'label': 'lung_022.nii.gz',
+    },
+    {
+      'image' : 'lung_023.nii.gz',
+      'boxes' : 'lung_023.nii.gz',
+      'label': 'lung_023.nii.gz',
+    },
+    {
+      'image' : 'lung_025.nii.gz',
+      'boxes' : 'lung_025.nii.gz',
+      'label': 'lung_025.nii.gz',
+    },
+    {
+      'image' : 'lung_026.nii.gz',
+      'boxes' : 'lung_026.nii.gz',
+      'label': 'lung_026.nii.gz',
+    },
+    {
+      'image' : 'lung_027.nii.gz',
+      'boxes' : 'lung_027.nii.gz',
+      'label': 'lung_027.nii.gz',
+    },
+    {
+      'image' : 'lung_028.nii.gz',
+      'boxes' : 'lung_028.nii.gz',
+      'label': 'lung_028.nii.gz',
+    },
+    {
+      'image' : 'lung_029.nii.gz',
+      'boxes' : 'lung_029.nii.gz',
+      'label': 'lung_029.nii.gz',
+    },
+    {
+      'image' : 'lung_031.nii.gz',
+      'boxes' : 'lung_031.nii.gz',
+      'label': 'lung_031.nii.gz',
+    },
+    {
+      'image' : 'lung_033.nii.gz',
+      'boxes' : 'lung_033.nii.gz',
+      'label': 'lung_033.nii.gz',
+    },
+    {
+      'image' : 'lung_034.nii.gz',
+      'boxes' : 'lung_034.nii.gz',
+      'label': 'lung_034.nii.gz',
+    },
+    {
+      'image' : 'lung_036.nii.gz',
+      'boxes' : 'lung_036.nii.gz',
+      'label': 'lung_036.nii.gz',
+    },
+    {
+      'image' : 'lung_037.nii.gz',
+      'boxes' : 'lung_037.nii.gz',
+      'label': 'lung_037.nii.gz',
+    },
+    {
+      'image' : 'lung_038.nii.gz',
+      'boxes' : 'lung_038.nii.gz',
+      'label': 'lung_038.nii.gz',
+    },
+    {
+      'image' : 'lung_041.nii.gz',
+      'boxes' : 'lung_041.nii.gz',
+      'label': 'lung_041.nii.gz',
+    },
+    {
+      'image' : 'lung_042.nii.gz',
+      'boxes' : 'lung_042.nii.gz',
+      'label': 'lung_042.nii.gz',
+    },
+    {
+      'image' : 'lung_043.nii.gz',
+      'boxes' : 'lung_043.nii.gz',
+      'label': 'lung_043.nii.gz',
+    },
+    {
+      'image' : 'lung_044.nii.gz',
+      'boxes' : 'lung_044.nii.gz',
+      'label': 'lung_044.nii.gz',
+    },
+    {
+      'image' : 'lung_045.nii.gz',
+      'boxes' : 'lung_045.nii.gz',
+      'label': 'lung_045.nii.gz',
+    },
+    {
+      'image' : 'lung_046.nii.gz',
+      'boxes' : 'lung_046.nii.gz',
+      'label': 'lung_046.nii.gz',
+    },
+    {
+      'image' : 'lung_047.nii.gz',
+      'boxes' : 'lung_047.nii.gz',
+      'label': 'lung_047.nii.gz',
+    },
+    {
+      'image' : 'lung_048.nii.gz',
+      'boxes' : 'lung_048.nii.gz',
+      'label': 'lung_048.nii.gz',
+    },
+    {
+      'image' : 'lung_049.nii.gz',
+      'boxes' : 'lung_049.nii.gz',
+      'label': 'lung_049.nii.gz',
+    },
+    {
+      'image' : 'lung_051.nii.gz',
+      'boxes' : 'lung_051.nii.gz',
+      'label': 'lung_051.nii.gz',
+    },
+    {
+      'image' : 'lung_053.nii.gz',
+      'boxes' : 'lung_053.nii.gz',
+      'label': 'lung_053.nii.gz',
+    },
+    {
+      'image' : 'lung_054.nii.gz',
+      'boxes' : 'lung_054.nii.gz',
+      'label': 'lung_054.nii.gz',
+    },
+    {
+      'image' : 'lung_055.nii.gz',
+      'boxes' : 'lung_055.nii.gz',
+      'label': 'lung_055.nii.gz',
+    },
+    {
+      'image' : 'lung_057.nii.gz',
+      'boxes' : 'lung_057.nii.gz',
+      'label': 'lung_057.nii.gz',
+    },
+    {
+      'image' : 'lung_058.nii.gz',
+      'boxes' : 'lung_058.nii.gz',
+      'label': 'lung_058.nii.gz',
+    },
+    {
+      'image' : 'lung_059.nii.gz',
+      'boxes' : 'lung_059.nii.gz',
+      'label': 'lung_059.nii.gz',
+    },
+    {
+      'image' : 'lung_061.nii.gz',
+      'boxes' : 'lung_061.nii.gz',
+      'label': 'lung_061.nii.gz',
+    },
+    {
+      'image' : 'lung_062.nii.gz',
+      'boxes' : 'lung_062.nii.gz',
+      'label': 'lung_062.nii.gz',
+    },
+    {
+      'image' : 'lung_064.nii.gz',
+      'boxes' : 'lung_064.nii.gz',
+      'label': 'lung_064.nii.gz',
+    },
+    {
+      'image' : 'lung_065.nii.gz',
+      'boxes' : 'lung_065.nii.gz',
+      'label': 'lung_065.nii.gz',
+    },
+    {
+      'image' : 'lung_066.nii.gz',
+      'boxes' : 'lung_066.nii.gz',
+      'label': 'lung_066.nii.gz',
+    },
+    {
+      'image' : 'lung_069.nii.gz',
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+      'boxes': 'G0014_scan1.nii.gz',
+      'label': 'G0014_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0014_scan3.nii.gz',
+      'boxes': 'G0014_scan3.nii.gz',
+      'label': 'G0014_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0016_scan2.nii.gz',
+      'boxes': 'G0016_scan2.nii.gz',
+      'label': 'G0016_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0017_scan2.nii.gz',
+      'boxes': 'G0017_scan2.nii.gz',
+      'label': 'G0017_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0018_scan2.nii.gz',
+      'boxes': 'G0018_scan2.nii.gz',
+      'label': 'G0018_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0019_scan1.nii.gz',
+      'boxes': 'G0019_scan1.nii.gz',
+      'label': 'G0019_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0019_scan2.nii.gz',
+      'boxes': 'G0019_scan2.nii.gz',
+      'label': 'G0019_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0020_scan1.nii.gz',
+      'boxes': 'G0020_scan1.nii.gz',
+      'label': 'G0020_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0020_scan2.nii.gz',
+      'boxes': 'G0020_scan2.nii.gz',
+      'label': 'G0020_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0021_scan2.nii.gz',
+      'boxes': 'G0021_scan2.nii.gz',
+      'label': 'G0021_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0022_scan2.nii.gz',
+      'boxes': 'G0022_scan2.nii.gz',
+      'label': 'G0022_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0023_scan2.nii.gz',
+      'boxes': 'G0023_scan2.nii.gz',
+      'label': 'G0023_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0024_scan1.nii.gz',
+      'boxes': 'G0024_scan1.nii.gz',
+      'label': 'G0024_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0024_scan2.nii.gz',
+      'boxes': 'G0024_scan2.nii.gz',
+      'label': 'G0024_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0025_scan2.nii.gz',
+      'boxes': 'G0025_scan2.nii.gz',
+      'label': 'G0025_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0026_scan2.nii.gz',
+      'boxes': 'G0026_scan2.nii.gz',
+      'label': 'G0026_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0027_scan2.nii.gz',
+      'boxes': 'G0027_scan2.nii.gz',
+      'label': 'G0027_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0028_scan2.nii.gz',
+      'boxes': 'G0028_scan2.nii.gz',
+      'label': 'G0028_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0029_scan2.nii.gz',
+      'boxes': 'G0029_scan2.nii.gz',
+      'label': 'G0029_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0030_scan4.nii.gz',
+      'boxes': 'G0030_scan4.nii.gz',
+      'label': 'G0030_scan4.nii.gz'
+    },
+    {
+      'image' : 'G0031_scan2.nii.gz',
+      'boxes': 'G0031_scan2.nii.gz',
+      'label': 'G0031_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0032_scan1.nii.gz',
+      'boxes': 'G0032_scan1.nii.gz',
+      'label': 'G0032_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0032_scan2.nii.gz',
+      'boxes': 'G0032_scan2.nii.gz',
+      'label': 'G0032_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0032_scan3.nii.gz',
+      'boxes': 'G0032_scan3.nii.gz',
+      'label': 'G0032_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0032_scan4.nii.gz',
+      'boxes': 'G0032_scan4.nii.gz',
+      'label': 'G0032_scan4.nii.gz'
+    },
+    {
+      'image' : 'G0033_scan1.nii.gz',
+      'boxes': 'G0033_scan1.nii.gz',
+      'label': 'G0033_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0034_scan1.nii.gz',
+      'boxes': 'G0034_scan1.nii.gz',
+      'label': 'G0034_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0034_scan3.nii.gz',
+      'boxes': 'G0034_scan3.nii.gz',
+      'label': 'G0034_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0035_scan1.nii.gz',
+      'boxes': 'G0035_scan1.nii.gz',
+      'label': 'G0035_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0036_scan1.nii.gz',
+      'boxes': 'G0036_scan1.nii.gz',
+      'label': 'G0036_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0036_scan3.nii.gz',
+      'boxes': 'G0036_scan3.nii.gz',
+      'label': 'G0036_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0037_scan1.nii.gz',
+      'boxes': 'G0037_scan1.nii.gz',
+      'label': 'G0037_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0037_scan4.nii.gz',
+      'boxes': 'G0037_scan4.nii.gz',
+      'label': 'G0037_scan4.nii.gz'
+    },
+    {
+      'image' : 'G0038_scan1.nii.gz',
+      'boxes': 'G0038_scan1.nii.gz',
+      'label': 'G0038_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0038_scan2.nii.gz',
+      'boxes': 'G0038_scan2.nii.gz',
+      'label': 'G0038_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0039_scan1.nii.gz',
+      'boxes': 'G0039_scan1.nii.gz',
+      'label': 'G0039_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0039_scan3.nii.gz',
+      'boxes': 'G0039_scan3.nii.gz',
+      'label': 'G0039_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0040_scan1.nii.gz',
+      'boxes': 'G0040_scan1.nii.gz',
+      'label': 'G0040_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0040_scan3.nii.gz',
+      'boxes': 'G0040_scan3.nii.gz',
+      'label': 'G0040_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0041_scan2.nii.gz',
+      'boxes': 'G0041_scan2.nii.gz',
+      'label': 'G0041_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0041_scan3.nii.gz',
+      'boxes': 'G0041_scan3.nii.gz',
+      'label': 'G0041_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0042_scan1.nii.gz',
+      'boxes': 'G0042_scan1.nii.gz',
+      'label': 'G0042_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0042_scan3.nii.gz',
+      'boxes': 'G0042_scan3.nii.gz',
+      'label': 'G0042_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0043_scan1.nii.gz',
+      'boxes': 'G0043_scan1.nii.gz',
+      'label': 'G0043_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0044_scan1.nii.gz',
+      'boxes': 'G0044_scan1.nii.gz',
+      'label': 'G0044_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0044_scan3.nii.gz',
+      'boxes': 'G0044_scan3.nii.gz',
+      'label': 'G0044_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0045_scan1.nii.gz',
+      'boxes': 'G0045_scan1.nii.gz',
+      'label': 'G0045_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0045_scan3.nii.gz',
+      'boxes': 'G0045_scan3.nii.gz',
+      'label': 'G0045_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0046_scan1.nii.gz',
+      'boxes': 'G0046_scan1.nii.gz',
+      'label': 'G0046_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0046_scan3.nii.gz',
+      'boxes': 'G0046_scan3.nii.gz',
+      'label': 'G0046_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0047_scan1.nii.gz',
+      'boxes': 'G0047_scan1.nii.gz',
+      'label': 'G0047_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0048_scan1.nii.gz',
+      'boxes': 'G0048_scan1.nii.gz',
+      'label': 'G0048_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0049_scan1.nii.gz',
+      'boxes': 'G0049_scan1.nii.gz',
+      'label': 'G0049_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0049_scan3.nii.gz',
+      'boxes': 'G0049_scan3.nii.gz',
+      'label': 'G0049_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0050_scan1.nii.gz',
+      'boxes': 'G0050_scan1.nii.gz',
+      'label': 'G0050_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0050_scan3.nii.gz',
+      'boxes': 'G0050_scan3.nii.gz',
+      'label': 'G0050_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0051_scan1.nii.gz',
+      'boxes': 'G0051_scan1.nii.gz',
+      'label': 'G0051_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0051_scan3.nii.gz',
+      'boxes': 'G0051_scan3.nii.gz',
+      'label': 'G0051_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0052_scan1.nii.gz',
+      'boxes': 'G0052_scan1.nii.gz',
+      'label': 'G0052_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0052_scan3.nii.gz',
+      'boxes': 'G0052_scan3.nii.gz',
+      'label': 'G0052_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0053_scan2.nii.gz',
+      'boxes': 'G0053_scan2.nii.gz',
+      'label': 'G0053_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0053_scan3.nii.gz',
+      'boxes': 'G0053_scan3.nii.gz',
+      'label': 'G0053_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0054_scan1.nii.gz',
+      'boxes': 'G0054_scan1.nii.gz',
+      'label': 'G0054_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0054_scan2.nii.gz',
+      'boxes': 'G0054_scan2.nii.gz',
+      'label': 'G0054_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0055_scan1.nii.gz',
+      'boxes': 'G0055_scan1.nii.gz',
+      'label': 'G0055_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0055_scan3.nii.gz',
+      'boxes': 'G0055_scan3.nii.gz',
+      'label': 'G0055_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0056_scan1.nii.gz',
+      'boxes': 'G0056_scan1.nii.gz',
+      'label': 'G0056_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0056_scan3.nii.gz',
+      'boxes': 'G0056_scan3.nii.gz',
+      'label': 'G0056_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0057_scan1.nii.gz',
+      'boxes': 'G0057_scan1.nii.gz',
+      'label': 'G0057_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0057_scan3.nii.gz',
+      'boxes': 'G0057_scan3.nii.gz',
+      'label': 'G0057_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0058_scan1.nii.gz',
+      'boxes': 'G0058_scan1.nii.gz',
+      'label': 'G0058_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0058_scan3.nii.gz',
+      'boxes': 'G0058_scan3.nii.gz',
+      'label': 'G0058_scan3.nii.gz'
+    },
+    {
+      'image' : 'G0059_scan1.nii.gz',
+      'boxes': 'G0059_scan1.nii.gz',
+      'label': 'G0059_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0059_scan2.nii.gz',
+      'boxes': 'G0059_scan2.nii.gz',
+      'label': 'G0059_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0060_scan1.nii.gz',
+      'boxes': 'G0060_scan1.nii.gz',
+      'label': 'G0060_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0060_scan2.nii.gz',
+      'boxes': 'G0060_scan2.nii.gz',
+      'label': 'G0060_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0062_scan1.nii.gz',
+      'boxes': 'G0062_scan1.nii.gz',
+      'label': 'G0062_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0062_scan3.nii.gz',
+      'boxes': 'G0062_scan3.nii.gz',
+      'label': 'G0062_scan3.nii.gz'
+    },
+
+]
+
+val: [
+    {
+      'image' : 'lung_073.nii.gz',
+      'boxes' : 'lung_073.nii.gz',
+      'label': 'lung_073.nii.gz',
+    },
+    {
+      'image' : 'lung_074.nii.gz',
+      'boxes' : 'lung_074.nii.gz',
+      'label': 'lung_074.nii.gz',
+    },
+    {
+      'image' : 'lung_075.nii.gz',
+      'boxes' : 'lung_075.nii.gz',
+      'label': 'lung_075.nii.gz',
+    },
+    {
+      'image' : 'lung_078.nii.gz',
+      'boxes' : 'lung_078.nii.gz',
+      'label': 'lung_078.nii.gz',
+    },
+    {
+      'image' : 'lung_079.nii.gz',
+      'boxes' : 'lung_079.nii.gz',
+      'label': 'lung_079.nii.gz',
+    }
+]
+
+
+val2: [
+    {
+      'image' : 'A0110_scan1.nii.gz',
+      'boxes': 'A0110_scan1.nii.gz',
+      'label': 'A0110_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0110_scan2.nii.gz',
+      'boxes': 'A0110_scan2.nii.gz',
+      'label': 'A0110_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0110_scan3.nii.gz',
+      'boxes': 'A0110_scan3.nii.gz',
+      'label': 'A0110_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0111_scan1.nii.gz',
+      'boxes': 'A0111_scan1.nii.gz',
+      'label': 'A0111_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0111_scan2.nii.gz',
+      'boxes': 'A0111_scan2.nii.gz',
+      'label': 'A0111_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0111_scan3.nii.gz',
+      'boxes': 'A0111_scan3.nii.gz',
+      'label': 'A0111_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0112_scan2.nii.gz',
+      'boxes': 'A0112_scan2.nii.gz',
+      'label': 'A0112_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0112_scan3.nii.gz',
+      'boxes': 'A0112_scan3.nii.gz',
+      'label': 'A0112_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0112_scan4.nii.gz',
+      'boxes': 'A0112_scan4.nii.gz',
+      'label': 'A0112_scan4.nii.gz'
+    },
+    {
+      'image' : 'A0113_scan1.nii.gz',
+      'boxes': 'A0113_scan1.nii.gz',
+      'label': 'A0113_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0113_scan2.nii.gz',
+      'boxes': 'A0113_scan2.nii.gz',
+      'label': 'A0113_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0113_scan3.nii.gz',
+      'boxes': 'A0113_scan3.nii.gz',
+      'label': 'A0113_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0113_scan4.nii.gz',
+      'boxes': 'A0113_scan4.nii.gz',
+      'label': 'A0113_scan4.nii.gz'
+    },
+    {
+      'image' : 'A0113_scan5.nii.gz',
+      'boxes': 'A0113_scan5.nii.gz',
+      'label': 'A0113_scan5.nii.gz'
+    },
+    {
+      'image' : 'A0114_scan2.nii.gz',
+      'boxes': 'A0114_scan2.nii.gz',
+      'label': 'A0114_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0115_scan2.nii.gz',
+      'boxes': 'A0115_scan2.nii.gz',
+      'label': 'A0115_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0116_scan1.nii.gz',
+      'boxes': 'A0116_scan1.nii.gz',
+      'label': 'A0116_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0116_scan2.nii.gz',
+      'boxes': 'A0116_scan2.nii.gz',
+      'label': 'A0116_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0117_scan1.nii.gz',
+      'boxes': 'A0117_scan1.nii.gz',
+      'label': 'A0117_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0117_scan2.nii.gz',
+      'boxes': 'A0117_scan2.nii.gz',
+      'label': 'A0117_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0117_scan3.nii.gz',
+      'boxes': 'A0117_scan3.nii.gz',
+      'label': 'A0117_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0117_scan4.nii.gz',
+      'boxes': 'A0117_scan4.nii.gz',
+      'label': 'A0117_scan4.nii.gz'
+    },
+    {
+      'image' : 'A0117_scan5.nii.gz',
+      'boxes': 'A0117_scan5.nii.gz',
+      'label': 'A0117_scan5.nii.gz'
+    },
+    {
+      'image' : 'A0117_scan6.nii.gz',
+      'boxes': 'A0117_scan6.nii.gz',
+      'label': 'A0117_scan6.nii.gz'
+    },
+    {
+      'image' : 'A0119_scan1.nii.gz',
+      'boxes': 'A0119_scan1.nii.gz',
+      'label': 'A0119_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0119_scan2.nii.gz',
+      'boxes': 'A0119_scan2.nii.gz',
+      'label': 'A0119_scan2.nii.gz'
+    }
+]

training/Resources/Idun_Train_2/lungdx_data.yaml

@@ -0,0 +1,2571 @@
+image_prefix: "/cluster/work/sosevle/full_data/LungDx-Lung/Images/"
+boxes_prefix: "/cluster/work/sosevle/full_data/LungDx-Lung/Boxes/"
+label_prefix: "/cluster/work/sosevle/full_data/LungDx-Lung/Labels/"
+
+train:
+  [
+    {
+      'image' : 'A0001_scan1_left.nii.gz',
+      'label': 'A0001_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0001_scan1_right.nii.gz',
+      'label': 'A0001_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0001_scan2_left.nii.gz',
+      'label': 'A0001_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0002_scan1_right.nii.gz',
+      'label': 'A0002_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0002_scan2_left.nii.gz',
+      'label': 'A0002_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0002_scan2_right.nii.gz',
+      'label': 'A0002_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0003_scan1_left.nii.gz',
+      'label': 'A0003_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0003_scan2_left.nii.gz',
+      'label': 'A0003_scan2_left.nii.gz'
+    },
+    {
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+    },
+    {
+      'image' : 'B0034_scan2_left.nii.gz',
+      'label': 'B0034_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'B0036_scan1_left.nii.gz',
+      'label': 'B0036_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'B0036_scan2_left.nii.gz',
+      'label': 'B0036_scan2_left.nii.gz'
+    }
+  ]
+
+test :
+  [
+    {
+      'image' : 'A0201_scan1_right.nii.gz',
+      'label': 'A0201_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0201_scan2_right.nii.gz',
+      'label': 'A0201_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0202_scan1_right.nii.gz',
+      'label': 'A0202_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0202_scan2_right.nii.gz',
+      'label': 'A0202_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0203_scan1_left.nii.gz',
+      'label': 'A0203_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0203_scan2_left.nii.gz',
+      'label': 'A0203_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0204_scan1_left.nii.gz',
+      'label': 'A0204_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0204_scan2_left.nii.gz',
+      'label': 'A0204_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0205_scan1_left.nii.gz',
+      'label': 'A0205_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0205_scan2_left.nii.gz',
+      'label': 'A0205_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0206_scan1_left.nii.gz',
+      'label': 'A0206_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0206_scan2_left.nii.gz',
+      'label': 'A0206_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0208_scan1_right.nii.gz',
+      'label': 'A0208_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0208_scan2_right.nii.gz',
+      'label': 'A0208_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0210_scan1_right.nii.gz',
+      'label': 'A0210_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0210_scan2_right.nii.gz',
+      'label': 'A0210_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0053_scan1_right.nii.gz',
+      'label': 'G0053_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0053_scan2_right.nii.gz',
+      'label': 'G0053_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0054_scan1_left.nii.gz',
+      'label': 'G0054_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0054_scan2_left.nii.gz',
+      'label': 'G0054_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'B0025_scan1_right.nii.gz',
+      'label': 'B0025_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'B0026_scan1_left.nii.gz',
+      'label': 'B0026_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'B0026_scan2_left.nii.gz',
+      'label': 'B0026_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'B0027_scan1_right.nii.gz',
+      'label': 'B0027_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'B0027_scan2_right.nii.gz',
+      'label': 'B0027_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'B0028_scan1_left.nii.gz',
+      'label': 'B0028_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'B0028_scan2_left.nii.gz',
+      'label': 'B0028_scan2_left.nii.gz'
+    }
+  ]

training/Resources/Idun_Train_2/msd_data.yaml

@@ -0,0 +1,243 @@
+image_prefix: "/cluster/work/sosevle/full_data/MSD-Lung/Images/"
+boxes_prefix: "/cluster/work/sosevle/full_data/MSD-Lung/Boxes/"
+label_prefix: "/cluster/work/sosevle/full_data/MSD-Lung/Labels/"
+
+train:
+  [
+    {
+      'image' : 'lung_001_right.nii.gz',
+      'label': 'lung_001_right.nii.gz'
+    },
+    {
+      'image' : 'lung_003_left.nii.gz',
+      'label': 'lung_003_left.nii.gz'
+    },
+    {
+      'image' : 'lung_004_left.nii.gz',
+      'label': 'lung_004_left.nii.gz'
+    },
+    {
+      'image' : 'lung_005_right.nii.gz',
+      'label': 'lung_005_right.nii.gz'
+    },
+    {
+      'image' : 'lung_006_left.nii.gz',
+      'label': 'lung_006_left.nii.gz'
+    },
+    {
+      'image' : 'lung_009_right.nii.gz',
+      'label': 'lung_009_right.nii.gz'
+    },
+    {
+      'image' : 'lung_010_left.nii.gz',
+      'label': 'lung_010_left.nii.gz'
+    },
+    {
+      'image' : 'lung_014_right.nii.gz',
+      'label': 'lung_014_right.nii.gz'
+    },
+    {
+      'image' : 'lung_015_right.nii.gz',
+      'label': 'lung_015_right.nii.gz'
+    },
+    {
+      'image' : 'lung_016_left.nii.gz',
+      'label': 'lung_016_left.nii.gz'
+    },
+    {
+      'image' : 'lung_018_right.nii.gz',
+      'label': 'lung_018_right.nii.gz'
+    },
+    {
+      'image' : 'lung_020_right.nii.gz',
+      'label': 'lung_020_right.nii.gz'
+    },
+    {
+      'image' : 'lung_022_right.nii.gz',
+      'label': 'lung_022_right.nii.gz'
+    },
+    {
+      'image' : 'lung_023_left.nii.gz',
+      'label': 'lung_023_left.nii.gz'
+    },
+    {
+      'image' : 'lung_023_right.nii.gz',
+      'label': 'lung_023_right.nii.gz'
+    },
+    {
+      'image' : 'lung_025_left.nii.gz',
+      'label': 'lung_025_left.nii.gz'
+    },
+    {
+      'image' : 'lung_026_right.nii.gz',
+      'label': 'lung_026_right.nii.gz'
+    },
+    {
+      'image' : 'lung_027_right.nii.gz',
+      'label': 'lung_027_right.nii.gz'
+    },
+    {
+      'image' : 'lung_028_left.nii.gz',
+      'label': 'lung_028_left.nii.gz'
+    },
+    {
+      'image' : 'lung_029_left.nii.gz',
+      'label': 'lung_029_left.nii.gz'
+    },
+    {
+      'image' : 'lung_031_right.nii.gz',
+      'label': 'lung_031_right.nii.gz'
+    },
+    {
+      'image' : 'lung_033_left.nii.gz',
+      'label': 'lung_033_left.nii.gz'
+    },
+    {
+      'image' : 'lung_034_right.nii.gz',
+      'label': 'lung_034_right.nii.gz'
+    },
+    {
+      'image' : 'lung_036_left.nii.gz',
+      'label': 'lung_036_left.nii.gz'
+    },
+    {
+      'image' : 'lung_037_right.nii.gz',
+      'label': 'lung_037_right.nii.gz'
+    },
+    {
+      'image' : 'lung_038_right.nii.gz',
+      'label': 'lung_038_right.nii.gz'
+    },
+    {
+      'image' : 'lung_041_right.nii.gz',
+      'label': 'lung_041_right.nii.gz'
+    },
+    {
+      'image' : 'lung_042_right.nii.gz',
+      'label': 'lung_042_right.nii.gz'
+    },
+    {
+      'image' : 'lung_043_right.nii.gz',
+      'label': 'lung_043_right.nii.gz'
+    },
+    {
+      'image' : 'lung_044_left.nii.gz',
+      'label': 'lung_044_left.nii.gz'
+    },
+    {
+      'image' : 'lung_045_left.nii.gz',
+      'label': 'lung_045_left.nii.gz'
+    },
+    {
+      'image' : 'lung_046_left.nii.gz',
+      'label': 'lung_046_left.nii.gz'
+    },
+    {
+      'image' : 'lung_047_right.nii.gz',
+      'label': 'lung_047_right.nii.gz'
+    },
+    {
+      'image' : 'lung_048_right.nii.gz',
+      'label': 'lung_048_right.nii.gz'
+    },
+    {
+      'image' : 'lung_049_right.nii.gz',
+      'label': 'lung_049_right.nii.gz'
+    },
+    {
+      'image' : 'lung_051_left.nii.gz',
+      'label': 'lung_051_left.nii.gz'
+    },
+    {
+      'image' : 'lung_053_right.nii.gz',
+      'label': 'lung_053_right.nii.gz'
+    },
+    {
+      'image' : 'lung_054_right.nii.gz',
+      'label': 'lung_054_right.nii.gz'
+    },
+    {
+      'image' : 'lung_055_left.nii.gz',
+      'label': 'lung_055_left.nii.gz'
+    },
+    {
+      'image' : 'lung_057_left.nii.gz',
+      'label': 'lung_057_left.nii.gz'
+    },
+    {
+      'image' : 'lung_058_right.nii.gz',
+      'label': 'lung_058_right.nii.gz'
+    },
+    {
+      'image' : 'lung_059_left.nii.gz',
+      'label': 'lung_059_left.nii.gz'
+    },
+    {
+      'image' : 'lung_061_left.nii.gz',
+      'label': 'lung_061_left.nii.gz'
+    },
+    {
+      'image' : 'lung_062_left.nii.gz',
+      'label': 'lung_062_left.nii.gz'
+    },
+    {
+      'image' : 'lung_064_left.nii.gz',
+      'label': 'lung_064_left.nii.gz'
+    },
+    {
+      'image' : 'lung_065_left.nii.gz',
+      'label': 'lung_065_left.nii.gz'
+    },
+    {
+      'image' : 'lung_066_left.nii.gz',
+      'label': 'lung_066_left.nii.gz'
+    },
+    {
+      'image' : 'lung_066_right.nii.gz',
+      'label': 'lung_066_right.nii.gz'
+    },
+    {
+      'image' : 'lung_069_right.nii.gz',
+      'label': 'lung_069_right.nii.gz'
+    },
+    {
+      'image' : 'lung_070_left.nii.gz',
+      'label': 'lung_070_left.nii.gz'
+    },
+    {
+      'image' : 'lung_071_right.nii.gz',
+      'label': 'lung_071_right.nii.gz'
+    },
+    {
+      'image' : 'lung_073_left.nii.gz',
+      'label': 'lung_073_left.nii.gz'
+    },
+    {
+      'image' : 'lung_074_left.nii.gz',
+      'label': 'lung_074_left.nii.gz'
+    },
+  ]
+
+val : 
+  [
+    {
+      'image' : 'lung_075_left.nii.gz',
+      'label': 'lung_075_left.nii.gz'
+    },
+    {
+      'image' : 'lung_078_left.nii.gz',
+      'label': 'lung_078_left.nii.gz'
+    },
+    {
+      'image' : 'lung_079_right.nii.gz',
+      'label': 'lung_079_right.nii.gz'
+    },
+    {
+      'image' : 'lung_080_left.nii.gz',
+      'label': 'lung_080_left.nii.gz'
+    },
+    {
+      'image' : 'lung_081_right.nii.gz',
+      'label': 'lung_081_right.nii.gz'
+    }
+  ]

training/Resources/Idun_Train_2/train_16.yaml

@@ -0,0 +1,28 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_2/data.yaml"
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet4
+  loss: DiceLoss
+  weights: 
+  filter_base: 32
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.001
+
+train:
+  max_epochs: 5000
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t16.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/16/"
+  batch_size: 10

training/Resources/Idun_Train_2/train_32.yaml

@@ -0,0 +1,28 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_2/data.yaml"
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet4
+  loss: DiceLoss
+  weights: 
+  filter_base: 32
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.0001
+
+train:
+  max_epochs: 5000
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t32.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/32/"
+  batch_size: 10

training/Resources/Idun_Train_2/train_48.yaml

@@ -0,0 +1,28 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_2/data.yaml"
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet4
+  loss: DiceLoss
+  weights: 
+  filter_base: 32
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.00001
+
+train:
+  max_epochs: 5000
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t48.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/48/"
+  batch_size: 10

training/Resources/Idun_Train_2/train_64.yaml

@@ -0,0 +1,28 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_2/data.yaml"
+  aug_prob: 0.5
+  scale_dim:
+    d_0: 176
+    d_1: 176
+    d_2: 96
+
+model:
+  architecture: UNet4
+  loss: DiceLoss
+  weights: 
+  filter_base: 64
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.00012
+
+train:
+  max_epochs: 5000
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t64.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/64/"
+  batch_size: 10

training/Resources/Idun_Train_2/train_focal.yaml

@@ -0,0 +1,28 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_1/full_size.yaml"
+  aug_prob: 0.15
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet_test
+  loss: DiceLoss
+  weights: 
+  filter_base: 32
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.00025
+
+train:
+  max_epochs: 5000
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t_test2.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/t_test2/"
+  batch_size: 10

training/Resources/Idun_Train_2/train_tversky.yaml

@@ -0,0 +1,28 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_2/data.yaml"
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet4
+  loss: Tversky
+  weights: 
+  filter_base: 32
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.0004
+
+train:
+  max_epochs: 5000
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t_tver.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/t_tver/"
+  batch_size: 10

training/Resources/Idun_Train_2/train_ud.yaml

@@ -0,0 +1,29 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_2/data.yaml"
+  aug_prob: 0.2
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet_monai_double
+  loss: DiceLoss
+  weights: 
+  filter_base: 32
+  filter_expansion: 2
+  filters: [64, 128, 256, 512]
+  optimizer:
+    name: 'Adam'
+    lr: 0.00005
+
+train:
+  max_epochs: 5000
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t_ud.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/ud/"
+  batch_size: 10

training/Resources/Idun_Train_2/train_us.yaml

@@ -0,0 +1,29 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Idun_Train_2/data.yaml"
+  aug_prob: 0.2
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet_monai
+  loss: DiceLoss
+  weights: 
+  filter_base: 32
+  filter_expansion: 2
+  filters: [64, 128, 256, 512]
+  optimizer:
+    name: 'Adam'
+    lr: 0.00005
+
+train:
+  max_epochs: 5000
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/cluster/work/sosevle/metrics/t_us.csv"
+  save_frequency: 5
+  model_directory: "/cluster/work/sosevle/models/us/"
+  batch_size: 10

training/Resources/Inference/inference.yaml

@@ -0,0 +1,18 @@
+device: 'cuda'
+
+data:
+  dataset: "/cluster/work/sosevle/LungTumorSegmentation/Resources/Inference/teacher_inference_data.yaml"
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet_test
+  weights: "/cluster/work/sosevle/model_last_128.pth"
+  loss: DiceLoss
+  filter_base: 128
+  filter_expansion: 2
+
+inference:
+  save_directory: '/cluster/work/sosevle/Lung-Dx_Cropped/Cropped/Labels/'

training/Resources/Inference/malvik.yaml

@@ -0,0 +1,18 @@
+device: 'cuda'
+
+data:
+  dataset: "/home/tumor/LungTumorSegmentation/Resources/Inference/malvik_inference_data2.yaml"
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet_test
+  weights: "/home/tumor/model_last_128.pth"
+  loss: DiceLoss
+  filter_base: 128
+  filter_expansion: 2
+
+inference:
+  save_directory: '/home/tumor/data/SoftLabels/'

training/Resources/Inference/malvik_inference_data.yaml

@@ -0,0 +1,2379 @@
+image_prefix: "/home/tumor/data/for_annotation/Images/"
+label_prefix: ""
+boxes_prefix: "/home/tumor/data/for_annotation//Boxes/"
+data:
+  [
+    {
+      'image' : 'A0001_scan1.nii',
+      'boxes': 'A0001_scan1.nii'
+    },
+    {
+      'image' : 'A0001_scan2.nii',
+      'boxes': 'A0001_scan2.nii'
+    },
+    {
+      'image' : 'A0002_scan1.nii',
+      'boxes': 'A0002_scan1.nii'
+    },
+    {
+      'image' : 'A0002_scan2.nii',
+      'boxes': 'A0002_scan2.nii'
+    },
+    {
+      'image' : 'A0003_scan1.nii',
+      'boxes': 'A0003_scan1.nii'
+    },
+    {
+      'image' : 'A0003_scan2.nii',
+      'boxes': 'A0003_scan2.nii'
+    },
+    {
+      'image' : 'A0004_scan1.nii',
+      'boxes': 'A0004_scan1.nii'
+    },
+    {
+      'image' : 'A0004_scan2.nii',
+      'boxes': 'A0004_scan2.nii'
+    },
+    {
+      'image' : 'A0006_scan1.nii',
+      'boxes': 'A0006_scan1.nii'
+    },
+    {
+      'image' : 'A0006_scan4.nii',
+      'boxes': 'A0006_scan4.nii'
+    },
+    {
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+    },
+    {
+      'image' : 'G0032_scan4.nii',
+      'boxes': 'G0032_scan4.nii'
+    },
+    {
+      'image' : 'G0033_scan1.nii',
+      'boxes': 'G0033_scan1.nii'
+    },
+    {
+      'image' : 'G0034_scan1.nii',
+      'boxes': 'G0034_scan1.nii'
+    },
+    {
+      'image' : 'G0034_scan3.nii',
+      'boxes': 'G0034_scan3.nii'
+    },
+    {
+      'image' : 'G0035_scan1.nii',
+      'boxes': 'G0035_scan1.nii'
+    },
+    {
+      'image' : 'G0036_scan1.nii',
+      'boxes': 'G0036_scan1.nii'
+    },
+    {
+      'image' : 'G0036_scan3.nii',
+      'boxes': 'G0036_scan3.nii'
+    },
+    {
+      'image' : 'G0037_scan1.nii',
+      'boxes': 'G0037_scan1.nii'
+    },
+    {
+      'image' : 'G0037_scan4.nii',
+      'boxes': 'G0037_scan4.nii'
+    },
+    {
+      'image' : 'G0038_scan1.nii',
+      'boxes': 'G0038_scan1.nii'
+    },
+    {
+      'image' : 'G0038_scan2.nii',
+      'boxes': 'G0038_scan2.nii'
+    },
+    {
+      'image' : 'G0039_scan1.nii',
+      'boxes': 'G0039_scan1.nii'
+    },
+    {
+      'image' : 'G0039_scan3.nii',
+      'boxes': 'G0039_scan3.nii'
+    },
+    {
+      'image' : 'G0040_scan1.nii',
+      'boxes': 'G0040_scan1.nii'
+    },
+    {
+      'image' : 'G0040_scan3.nii',
+      'boxes': 'G0040_scan3.nii'
+    },
+    {
+      'image' : 'G0041_scan2.nii',
+      'boxes': 'G0041_scan2.nii'
+    },
+    {
+      'image' : 'G0041_scan3.nii',
+      'boxes': 'G0041_scan3.nii'
+    },
+    {
+      'image' : 'G0042_scan1.nii',
+      'boxes': 'G0042_scan1.nii'
+    },
+    {
+      'image' : 'G0042_scan3.nii',
+      'boxes': 'G0042_scan3.nii'
+    },
+    {
+      'image' : 'G0043_scan1.nii',
+      'boxes': 'G0043_scan1.nii'
+    },
+    {
+      'image' : 'G0043_scan3.nii',
+      'boxes': 'G0043_scan3.nii'
+    },
+    {
+      'image' : 'G0044_scan1.nii',
+      'boxes': 'G0044_scan1.nii'
+    },
+    {
+      'image' : 'G0044_scan3.nii',
+      'boxes': 'G0044_scan3.nii'
+    },
+    {
+      'image' : 'G0045_scan1.nii',
+      'boxes': 'G0045_scan1.nii'
+    },
+    {
+      'image' : 'G0045_scan3.nii',
+      'boxes': 'G0045_scan3.nii'
+    },
+    {
+      'image' : 'G0046_scan1.nii',
+      'boxes': 'G0046_scan1.nii'
+    },
+    {
+      'image' : 'G0046_scan3.nii',
+      'boxes': 'G0046_scan3.nii'
+    },
+    {
+      'image' : 'G0047_scan1.nii',
+      'boxes': 'G0047_scan1.nii'
+    },
+    {
+      'image' : 'G0048_scan1.nii',
+      'boxes': 'G0048_scan1.nii'
+    },
+    {
+      'image' : 'G0049_scan1.nii',
+      'boxes': 'G0049_scan1.nii'
+    },
+    {
+      'image' : 'G0049_scan3.nii',
+      'boxes': 'G0049_scan3.nii'
+    },
+    {
+      'image' : 'G0050_scan1.nii',
+      'boxes': 'G0050_scan1.nii'
+    },
+    {
+      'image' : 'G0050_scan3.nii',
+      'boxes': 'G0050_scan3.nii'
+    },
+    {
+      'image' : 'G0051_scan1.nii',
+      'boxes': 'G0051_scan1.nii'
+    },
+    {
+      'image' : 'G0051_scan3.nii',
+      'boxes': 'G0051_scan3.nii'
+    },
+    {
+      'image' : 'G0052_scan1.nii',
+      'boxes': 'G0052_scan1.nii'
+    },
+    {
+      'image' : 'G0052_scan3.nii',
+      'boxes': 'G0052_scan3.nii'
+    },
+    {
+      'image' : 'G0053_scan2.nii',
+      'boxes': 'G0053_scan2.nii'
+    },
+    {
+      'image' : 'G0053_scan3.nii',
+      'boxes': 'G0053_scan3.nii'
+    },
+    {
+      'image' : 'G0054_scan1.nii',
+      'boxes': 'G0054_scan1.nii'
+    },
+    {
+      'image' : 'G0054_scan2.nii',
+      'boxes': 'G0054_scan2.nii'
+    },
+    {
+      'image' : 'G0055_scan1.nii',
+      'boxes': 'G0055_scan1.nii'
+    },
+    {
+      'image' : 'G0055_scan3.nii',
+      'boxes': 'G0055_scan3.nii'
+    },
+    {
+      'image' : 'G0056_scan1.nii',
+      'boxes': 'G0056_scan1.nii'
+    },
+    {
+      'image' : 'G0056_scan3.nii',
+      'boxes': 'G0056_scan3.nii'
+    },
+    {
+      'image' : 'G0057_scan1.nii',
+      'boxes': 'G0057_scan1.nii'
+    },
+    {
+      'image' : 'G0057_scan3.nii',
+      'boxes': 'G0057_scan3.nii'
+    },
+    {
+      'image' : 'G0058_scan1.nii',
+      'boxes': 'G0058_scan1.nii'
+    },
+    {
+      'image' : 'G0058_scan3.nii',
+      'boxes': 'G0058_scan3.nii'
+    },
+    {
+      'image' : 'G0059_scan1.nii',
+      'boxes': 'G0059_scan1.nii'
+    },
+    {
+      'image' : 'G0059_scan2.nii',
+      'boxes': 'G0059_scan2.nii'
+    },
+    {
+      'image' : 'G0060_scan1.nii',
+      'boxes': 'G0060_scan1.nii'
+    },
+    {
+      'image' : 'G0060_scan2.nii',
+      'boxes': 'G0060_scan2.nii'
+    },
+    {
+      'image' : 'G0062_scan1.nii',
+      'boxes': 'G0062_scan1.nii'
+    },
+    {
+      'image' : 'G0062_scan3.nii',
+      'boxes': 'G0062_scan3.nii'
+    },
+
+]

training/Resources/Inference/malvik_inference_data2.yaml

@@ -0,0 +1,42 @@
+image_prefix: "/home/tumor/data/for_annotation/Images/"
+label_prefix: ""
+boxes_prefix: "/home/tumor/data/for_annotation//Boxes/"
+data:
+  [
+    {
+      'image' : 'lung_092.nii',
+      'boxes' : 'lung_092.nii',
+      'label' : 'lung_092.nii'
+    },
+    {
+      'image' : 'lung_093.nii',
+      'boxes' : 'lung_093.nii',
+      'label' : 'lung_093.nii'
+    },
+    {
+      'image' : 'lung_095.nii',
+      'boxes' : 'lung_095.nii',
+      'label' : 'lung_095.nii'
+    },
+    {
+      'image' : 'lung_081.nii',
+      'boxes' : 'lung_081.nii',
+      'label' : 'lung_081.nii'
+    },
+    {
+      'image' : 'lung_083.nii',
+      'boxes' : 'lung_083.nii',
+      'label' : 'lung_083.nii'
+    },
+    {
+      'image' : 'lung_084.nii',
+      'boxes' : 'lung_084.nii',
+      'label' : 'lung_084.nii'
+    },
+    {
+      'image' : 'lung_086.nii',
+      'boxes' : 'lung_086.nii',
+      'label' : 'lung_086.nii'
+    }
+
+]

training/Resources/Inference/teacher_inference_data.yaml

@@ -0,0 +1,2379 @@
+image_prefix: "/cluster/work/sosevle/Lung-Dx_Cropped/Cropped/Images/"
+label_prefix: ""
+boxes_prefix: "/cluster/work/sosevle/Lung-Dx_Cropped/Cropped/Boxes/"
+data:
+  [
+    {
+      'image' : 'A0001_scan1.nii',
+      'boxes': 'A0001_scan1.nii'
+    },
+    {
+      'image' : 'A0001_scan2.nii',
+      'boxes': 'A0001_scan2.nii'
+    },
+    {
+      'image' : 'A0002_scan1.nii',
+      'boxes': 'A0002_scan1.nii'
+    },
+    {
+      'image' : 'A0002_scan2.nii',
+      'boxes': 'A0002_scan2.nii'
+    },
+    {
+      'image' : 'A0003_scan1.nii',
+      'boxes': 'A0003_scan1.nii'
+    },
+    {
+      'image' : 'A0003_scan2.nii',
+      'boxes': 'A0003_scan2.nii'
+    },
+    {
+      'image' : 'A0004_scan1.nii',
+      'boxes': 'A0004_scan1.nii'
+    },
+    {
+      'image' : 'A0004_scan2.nii',
+      'boxes': 'A0004_scan2.nii'
+    },
+    {
+      'image' : 'A0006_scan1.nii',
+      'boxes': 'A0006_scan1.nii'
+    },
+    {
+      'image' : 'A0006_scan4.nii',
+      'boxes': 'A0006_scan4.nii'
+    },
+    {
+      'image' : 'A0007_scan1.nii',
+      'boxes': 'A0007_scan1.nii'
+    },
+    {
+      'image' : 'A0007_scan2.nii',
+      'boxes': 'A0007_scan2.nii'
+    },
+    {
+      'image' : 'A0008_scan1.nii',
+      'boxes': 'A0008_scan1.nii'
+    },
+    {
+      'image' : 'A0008_scan2.nii',
+      'boxes': 'A0008_scan2.nii'
+    },
+    {
+      'image' : 'A0009_scan1.nii',
+      'boxes': 'A0009_scan1.nii'
+    },
+    {
+      'image' : 'A0009_scan2.nii',
+      'boxes': 'A0009_scan2.nii'
+    },
+    {
+      'image' : 'A0010_scan1.nii',
+      'boxes': 'A0010_scan1.nii'
+    },
+    {
+      'image' : 'A0010_scan2.nii',
+      'boxes': 'A0010_scan2.nii'
+    },
+    {
+      'image' : 'A0011_scan2.nii',
+      'boxes': 'A0011_scan2.nii'
+    },
+    {
+      'image' : 'A0011_scan4.nii',
+      'boxes': 'A0011_scan4.nii'
+    },
+    {
+      'image' : 'A0013_scan3.nii',
+      'boxes': 'A0013_scan3.nii'
+    },
+    {
+      'image' : 'A0014_scan2.nii',
+      'boxes': 'A0014_scan2.nii'
+    },
+    {
+      'image' : 'A0015_scan1.nii',
+      'boxes': 'A0015_scan1.nii'
+    },
+    {
+      'image' : 'A0015_scan2.nii',
+      'boxes': 'A0015_scan2.nii'
+    },
+    {
+      'image' : 'A0018_scan3.nii',
+      'boxes': 'A0018_scan3.nii'
+    },
+    {
+      'image' : 'A0018_scan4.nii',
+      'boxes': 'A0018_scan4.nii'
+    },
+    {
+      'image' : 'A0019_scan3.nii',
+      'boxes': 'A0019_scan3.nii'
+    },
+    {
+      'image' : 'A0019_scan4.nii',
+      'boxes': 'A0019_scan4.nii'
+    },
+    {
+      'image' : 'A0019_scan5.nii',
+      'boxes': 'A0019_scan5.nii'
+    },
+    {
+      'image' : 'A0022_scan2.nii',
+      'boxes': 'A0022_scan2.nii'
+    },
+    {
+      'image' : 'A0022_scan3.nii',
+      'boxes': 'A0022_scan3.nii'
+    },
+    {
+      'image' : 'A0022_scan4.nii',
+      'boxes': 'A0022_scan4.nii'
+    },
+    {
+      'image' : 'A0022_scan5.nii',
+      'boxes': 'A0022_scan5.nii'
+    },
+    {
+      'image' : 'A0023_scan2.nii',
+      'boxes': 'A0023_scan2.nii'
+    },
+    {
+      'image' : 'A0026_scan1.nii',
+      'boxes': 'A0026_scan1.nii'
+    },
+    {
+      'image' : 'A0026_scan2.nii',
+      'boxes': 'A0026_scan2.nii'
+    },
+    {
+      'image' : 'A0028_scan1.nii',
+      'boxes': 'A0028_scan1.nii'
+    },
+    {
+      'image' : 'A0028_scan2.nii',
+      'boxes': 'A0028_scan2.nii'
+    },
+    {
+      'image' : 'A0029_scan4.nii',
+      'boxes': 'A0029_scan4.nii'
+    },
+    {
+      'image' : 'A0029_scan5.nii',
+      'boxes': 'A0029_scan5.nii'
+    },
+    {
+      'image' : 'A0029_scan7.nii',
+      'boxes': 'A0029_scan7.nii'
+    },
+    {
+      'image' : 'A0029_scan8.nii',
+      'boxes': 'A0029_scan8.nii'
+    },
+    {
+      'image' : 'A0031_scan2.nii',
+      'boxes': 'A0031_scan2.nii'
+    },
+    {
+      'image' : 'A0032_scan2.nii',
+      'boxes': 'A0032_scan2.nii'
+    },
+    {
+      'image' : 'A0033_scan2.nii',
+      'boxes': 'A0033_scan2.nii'
+    },
+    {
+      'image' : 'A0034_scan1.nii',
+      'boxes': 'A0034_scan1.nii'
+    },
+    {
+      'image' : 'A0034_scan2.nii',
+      'boxes': 'A0034_scan2.nii'
+    },
+    {
+      'image' : 'A0035_scan1.nii',
+      'boxes': 'A0035_scan1.nii'
+    },
+    {
+      'image' : 'A0035_scan2.nii',
+      'boxes': 'A0035_scan2.nii'
+    },
+    {
+      'image' : 'A0036_scan2.nii',
+      'boxes': 'A0036_scan2.nii'
+    },
+    {
+      'image' : 'A0037_scan2.nii',
+      'boxes': 'A0037_scan2.nii'
+    },
+    {
+      'image' : 'A0039_scan3.nii',
+      'boxes': 'A0039_scan3.nii'
+    },
+    {
+      'image' : 'A0040_scan3.nii',
+      'boxes': 'A0040_scan3.nii'
+    },
+    {
+      'image' : 'A0041_scan2.nii',
+      'boxes': 'A0041_scan2.nii'
+    },
+    {
+      'image' : 'A0041_scan3.nii',
+      'boxes': 'A0041_scan3.nii'
+    },
+    {
+      'image' : 'A0041_scan4.nii',
+      'boxes': 'A0041_scan4.nii'
+    },
+    {
+      'image' : 'A0041_scan5.nii',
+      'boxes': 'A0041_scan5.nii'
+    },
+    {
+      'image' : 'A0042_scan3.nii',
+      'boxes': 'A0042_scan3.nii'
+    },
+    {
+      'image' : 'A0043_scan1.nii',
+      'boxes': 'A0043_scan1.nii'
+    },
+    {
+      'image' : 'A0043_scan2.nii',
+      'boxes': 'A0043_scan2.nii'
+    },
+    {
+      'image' : 'A0044_scan1.nii',
+      'boxes': 'A0044_scan1.nii'
+    },
+    {
+      'image' : 'A0044_scan2.nii',
+      'boxes': 'A0044_scan2.nii'
+    },
+    {
+      'image' : 'A0046_scan2.nii',
+      'boxes': 'A0046_scan2.nii'
+    },
+    {
+      'image' : 'A0047_scan1.nii',
+      'boxes': 'A0047_scan1.nii'
+    },
+    {
+      'image' : 'A0048_scan1.nii',
+      'boxes': 'A0048_scan1.nii'
+    },
+    {
+      'image' : 'A0048_scan2.nii',
+      'boxes': 'A0048_scan2.nii'
+    },
+    {
+      'image' : 'A0049_scan3.nii',
+      'boxes': 'A0049_scan3.nii'
+    },
+    {
+      'image' : 'A0051_scan1.nii',
+      'boxes': 'A0051_scan1.nii'
+    },
+    {
+      'image' : 'A0051_scan2.nii',
+      'boxes': 'A0051_scan2.nii'
+    },
+    {
+      'image' : 'A0053_scan3.nii',
+      'boxes': 'A0053_scan3.nii'
+    },
+    {
+      'image' : 'A0054_scan3.nii',
+      'boxes': 'A0054_scan3.nii'
+    },
+    {
+      'image' : 'A0056_scan1.nii',
+      'boxes': 'A0056_scan1.nii'
+    },
+    {
+      'image' : 'A0056_scan2.nii',
+      'boxes': 'A0056_scan2.nii'
+    },
+    {
+      'image' : 'A0059_scan1.nii',
+      'boxes': 'A0059_scan1.nii'
+    },
+    {
+      'image' : 'A0059_scan2.nii',
+      'boxes': 'A0059_scan2.nii'
+    },
+    {
+      'image' : 'A0061_scan2.nii',
+      'boxes': 'A0061_scan2.nii'
+    },
+    {
+      'image' : 'A0063_scan3.nii',
+      'boxes': 'A0063_scan3.nii'
+    },
+    {
+      'image' : 'A0063_scan4.nii',
+      'boxes': 'A0063_scan4.nii'
+    },
+    {
+      'image' : 'A0063_scan6.nii',
+      'boxes': 'A0063_scan6.nii'
+    },
+    {
+      'image' : 'A0063_scan7.nii',
+      'boxes': 'A0063_scan7.nii'
+    },
+    {
+      'image' : 'A0063_scan8.nii',
+      'boxes': 'A0063_scan8.nii'
+    },
+    {
+      'image' : 'A0064_scan2.nii',
+      'boxes': 'A0064_scan2.nii'
+    },
+    {
+      'image' : 'A0065_scan2.nii',
+      'boxes': 'A0065_scan2.nii'
+    },
+    {
+      'image' : 'A0069_scan2.nii',
+      'boxes': 'A0069_scan2.nii'
+    },
+    {
+      'image' : 'A0070_scan1.nii',
+      'boxes': 'A0070_scan1.nii'
+    },
+    {
+      'image' : 'A0070_scan3.nii',
+      'boxes': 'A0070_scan3.nii'
+    },
+    {
+      'image' : 'A0071_scan2.nii',
+      'boxes': 'A0071_scan2.nii'
+    },
+    {
+      'image' : 'A0073_scan1.nii',
+      'boxes': 'A0073_scan1.nii'
+    },
+    {
+      'image' : 'A0073_scan2.nii',
+      'boxes': 'A0073_scan2.nii'
+    },
+    {
+      'image' : 'A0074_scan1.nii',
+      'boxes': 'A0074_scan1.nii'
+    },
+    {
+      'image' : 'A0074_scan2.nii',
+      'boxes': 'A0074_scan2.nii'
+    },
+    {
+      'image' : 'A0075_scan2.nii',
+      'boxes': 'A0075_scan2.nii'
+    },
+    {
+      'image' : 'A0077_scan2.nii',
+      'boxes': 'A0077_scan2.nii'
+    },
+    {
+      'image' : 'A0078_scan3.nii',
+      'boxes': 'A0078_scan3.nii'
+    },
+    {
+      'image' : 'A0078_scan4.nii',
+      'boxes': 'A0078_scan4.nii'
+    },
+    {
+      'image' : 'A0078_scan5.nii',
+      'boxes': 'A0078_scan5.nii'
+    },
+    {
+      'image' : 'A0078_scan6.nii',
+      'boxes': 'A0078_scan6.nii'
+    },
+    {
+      'image' : 'A0080_scan1.nii',
+      'boxes': 'A0080_scan1.nii'
+    },
+    {
+      'image' : 'A0080_scan2.nii',
+      'boxes': 'A0080_scan2.nii'
+    },
+    {
+      'image' : 'A0080_scan3.nii',
+      'boxes': 'A0080_scan3.nii'
+    },
+    {
+      'image' : 'A0081_scan1.nii',
+      'boxes': 'A0081_scan1.nii'
+    },
+    {
+      'image' : 'A0081_scan2.nii',
+      'boxes': 'A0081_scan2.nii'
+    },
+    {
+      'image' : 'A0082_scan2.nii',
+      'boxes': 'A0082_scan2.nii'
+    },
+    {
+      'image' : 'A0083_scan1.nii',
+      'boxes': 'A0083_scan1.nii'
+    },
+    {
+      'image' : 'A0083_scan2.nii',
+      'boxes': 'A0083_scan2.nii'
+    },
+    {
+      'image' : 'A0083_scan3.nii',
+      'boxes': 'A0083_scan3.nii'
+    },
+    {
+      'image' : 'A0083_scan4.nii',
+      'boxes': 'A0083_scan4.nii'
+    },
+    {
+      'image' : 'A0083_scan5.nii',
+      'boxes': 'A0083_scan5.nii'
+    },
+    {
+      'image' : 'A0083_scan6.nii',
+      'boxes': 'A0083_scan6.nii'
+    },
+    {
+      'image' : 'A0084_scan1.nii',
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+    },
+    {
+      'image' : 'B0022_scan2.nii',
+      'boxes': 'B0022_scan2.nii'
+    },
+    {
+      'image' : 'B0023_scan1.nii',
+      'boxes': 'B0023_scan1.nii'
+    },
+    {
+      'image' : 'B0023_scan2.nii',
+      'boxes': 'B0023_scan2.nii'
+    },
+    {
+      'image' : 'B0024_scan1.nii',
+      'boxes': 'B0024_scan1.nii'
+    },
+    {
+      'image' : 'B0024_scan2.nii',
+      'boxes': 'B0024_scan2.nii'
+    },
+    {
+      'image' : 'B0025_scan2.nii',
+      'boxes': 'B0025_scan2.nii'
+    },
+    {
+      'image' : 'B0026_scan1.nii',
+      'boxes': 'B0026_scan1.nii'
+    },
+    {
+      'image' : 'B0026_scan2.nii',
+      'boxes': 'B0026_scan2.nii'
+    },
+    {
+      'image' : 'B0027_scan1.nii',
+      'boxes': 'B0027_scan1.nii'
+    },
+    {
+      'image' : 'B0027_scan2.nii',
+      'boxes': 'B0027_scan2.nii'
+    },
+    {
+      'image' : 'B0028_scan1.nii',
+      'boxes': 'B0028_scan1.nii'
+    },
+    {
+      'image' : 'B0028_scan2.nii',
+      'boxes': 'B0028_scan2.nii'
+    },
+    {
+      'image' : 'B0031_scan1.nii',
+      'boxes': 'B0031_scan1.nii'
+    },
+    {
+      'image' : 'B0031_scan2.nii',
+      'boxes': 'B0031_scan2.nii'
+    },
+    {
+      'image' : 'B0033_scan1.nii',
+      'boxes': 'B0033_scan1.nii'
+    },
+    {
+      'image' : 'B0033_scan3.nii',
+      'boxes': 'B0033_scan3.nii'
+    },
+    {
+      'image' : 'B0034_scan1.nii',
+      'boxes': 'B0034_scan1.nii'
+    },
+    {
+      'image' : 'B0034_scan3.nii',
+      'boxes': 'B0034_scan3.nii'
+    },
+    {
+      'image' : 'B0036_scan1.nii',
+      'boxes': 'B0036_scan1.nii'
+    },
+    {
+      'image' : 'B0036_scan2.nii',
+      'boxes': 'B0036_scan2.nii'
+    },
+    {
+      'image' : 'B0037_scan1.nii',
+      'boxes': 'B0037_scan1.nii'
+    },
+    {
+      'image' : 'B0037_scan2.nii',
+      'boxes': 'B0037_scan2.nii'
+    },
+    {
+      'image' : 'B0038_scan1.nii',
+      'boxes': 'B0038_scan1.nii'
+    },
+    {
+      'image' : 'B0038_scan2.nii',
+      'boxes': 'B0038_scan2.nii'
+    },
+    {
+      'image' : 'B0040_scan1.nii',
+      'boxes': 'B0040_scan1.nii'
+    },
+    {
+      'image' : 'B0040_scan2.nii',
+      'boxes': 'B0040_scan2.nii'
+    },
+    {
+      'image' : 'B0040_scan3.nii',
+      'boxes': 'B0040_scan3.nii'
+    },
+    {
+      'image' : 'B0040_scan4.nii',
+      'boxes': 'B0040_scan4.nii'
+    },
+    {
+      'image' : 'B0041_scan1.nii',
+      'boxes': 'B0041_scan1.nii'
+    },
+    {
+      'image' : 'B0041_scan2.nii',
+      'boxes': 'B0041_scan2.nii'
+    },
+    {
+      'image' : 'B0042_scan1.nii',
+      'boxes': 'B0042_scan1.nii'
+    },
+    {
+      'image' : 'B0043_scan1.nii',
+      'boxes': 'B0043_scan1.nii'
+    },
+    {
+      'image' : 'B0043_scan2.nii',
+      'boxes': 'B0043_scan2.nii'
+    },
+    {
+      'image' : 'B0044_scan2.nii',
+      'boxes': 'B0044_scan2.nii'
+    },
+    {
+      'image' : 'E0001_scan2.nii',
+      'boxes': 'E0001_scan2.nii'
+    },
+    {
+      'image' : 'E0002_scan1.nii',
+      'boxes': 'E0002_scan1.nii'
+    },
+    {
+      'image' : 'E0003_scan2.nii',
+      'boxes': 'E0003_scan2.nii'
+    },
+    {
+      'image' : 'E0004_scan1.nii',
+      'boxes': 'E0004_scan1.nii'
+    },
+    {
+      'image' : 'E0004_scan2.nii',
+      'boxes': 'E0004_scan2.nii'
+    },
+    {
+      'image' : 'E0005_scan3.nii',
+      'boxes': 'E0005_scan3.nii'
+    },
+    {
+      'image' : 'G0002_scan1.nii',
+      'boxes': 'G0002_scan1.nii'
+    },
+    {
+      'image' : 'G0002_scan2.nii',
+      'boxes': 'G0002_scan2.nii'
+    },
+    {
+      'image' : 'G0003_scan2.nii',
+      'boxes': 'G0003_scan2.nii'
+    },
+    {
+      'image' : 'G0004_scan2.nii',
+      'boxes': 'G0004_scan2.nii'
+    },
+    {
+      'image' : 'G0005_scan1.nii',
+      'boxes': 'G0005_scan1.nii'
+    },
+    {
+      'image' : 'G0005_scan2.nii',
+      'boxes': 'G0005_scan2.nii'
+    },
+    {
+      'image' : 'G0006_scan2.nii',
+      'boxes': 'G0006_scan2.nii'
+    },
+    {
+      'image' : 'G0007_scan1.nii',
+      'boxes': 'G0007_scan1.nii'
+    },
+    {
+      'image' : 'G0007_scan2.nii',
+      'boxes': 'G0007_scan2.nii'
+    },
+    {
+      'image' : 'G0008_scan1.nii',
+      'boxes': 'G0008_scan1.nii'
+    },
+    {
+      'image' : 'G0008_scan2.nii',
+      'boxes': 'G0008_scan2.nii'
+    },
+    {
+      'image' : 'G0009_scan1.nii',
+      'boxes': 'G0009_scan1.nii'
+    },
+    {
+      'image' : 'G0009_scan2.nii',
+      'boxes': 'G0009_scan2.nii'
+    },
+    {
+      'image' : 'G0011_scan2.nii',
+      'boxes': 'G0011_scan2.nii'
+    },
+    {
+      'image' : 'G0012_scan2.nii',
+      'boxes': 'G0012_scan2.nii'
+    },
+    {
+      'image' : 'G0013_scan2.nii',
+      'boxes': 'G0013_scan2.nii'
+    },
+    {
+      'image' : 'G0014_scan1.nii',
+      'boxes': 'G0014_scan1.nii'
+    },
+    {
+      'image' : 'G0014_scan3.nii',
+      'boxes': 'G0014_scan3.nii'
+    },
+    {
+      'image' : 'G0016_scan2.nii',
+      'boxes': 'G0016_scan2.nii'
+    },
+    {
+      'image' : 'G0017_scan2.nii',
+      'boxes': 'G0017_scan2.nii'
+    },
+    {
+      'image' : 'G0018_scan2.nii',
+      'boxes': 'G0018_scan2.nii'
+    },
+    {
+      'image' : 'G0019_scan1.nii',
+      'boxes': 'G0019_scan1.nii'
+    },
+    {
+      'image' : 'G0019_scan2.nii',
+      'boxes': 'G0019_scan2.nii'
+    },
+    {
+      'image' : 'G0020_scan1.nii',
+      'boxes': 'G0020_scan1.nii'
+    },
+    {
+      'image' : 'G0020_scan2.nii',
+      'boxes': 'G0020_scan2.nii'
+    },
+    {
+      'image' : 'G0021_scan2.nii',
+      'boxes': 'G0021_scan2.nii'
+    },
+    {
+      'image' : 'G0022_scan2.nii',
+      'boxes': 'G0022_scan2.nii'
+    },
+    {
+      'image' : 'G0023_scan2.nii',
+      'boxes': 'G0023_scan2.nii'
+    },
+    {
+      'image' : 'G0024_scan1.nii',
+      'boxes': 'G0024_scan1.nii'
+    },
+    {
+      'image' : 'G0024_scan2.nii',
+      'boxes': 'G0024_scan2.nii'
+    },
+    {
+      'image' : 'G0025_scan2.nii',
+      'boxes': 'G0025_scan2.nii'
+    },
+    {
+      'image' : 'G0026_scan2.nii',
+      'boxes': 'G0026_scan2.nii'
+    },
+    {
+      'image' : 'G0027_scan2.nii',
+      'boxes': 'G0027_scan2.nii'
+    },
+    {
+      'image' : 'G0028_scan2.nii',
+      'boxes': 'G0028_scan2.nii'
+    },
+    {
+      'image' : 'G0029_scan2.nii',
+      'boxes': 'G0029_scan2.nii'
+    },
+    {
+      'image' : 'G0030_scan4.nii',
+      'boxes': 'G0030_scan4.nii'
+    },
+    {
+      'image' : 'G0031_scan2.nii',
+      'boxes': 'G0031_scan2.nii'
+    },
+    {
+      'image' : 'G0032_scan1.nii',
+      'boxes': 'G0032_scan1.nii'
+    },
+    {
+      'image' : 'G0032_scan2.nii',
+      'boxes': 'G0032_scan2.nii'
+    },
+    {
+      'image' : 'G0032_scan3.nii',
+      'boxes': 'G0032_scan3.nii'
+    },
+    {
+      'image' : 'G0032_scan4.nii',
+      'boxes': 'G0032_scan4.nii'
+    },
+    {
+      'image' : 'G0033_scan1.nii',
+      'boxes': 'G0033_scan1.nii'
+    },
+    {
+      'image' : 'G0034_scan1.nii',
+      'boxes': 'G0034_scan1.nii'
+    },
+    {
+      'image' : 'G0034_scan3.nii',
+      'boxes': 'G0034_scan3.nii'
+    },
+    {
+      'image' : 'G0035_scan1.nii',
+      'boxes': 'G0035_scan1.nii'
+    },
+    {
+      'image' : 'G0036_scan1.nii',
+      'boxes': 'G0036_scan1.nii'
+    },
+    {
+      'image' : 'G0036_scan3.nii',
+      'boxes': 'G0036_scan3.nii'
+    },
+    {
+      'image' : 'G0037_scan1.nii',
+      'boxes': 'G0037_scan1.nii'
+    },
+    {
+      'image' : 'G0037_scan4.nii',
+      'boxes': 'G0037_scan4.nii'
+    },
+    {
+      'image' : 'G0038_scan1.nii',
+      'boxes': 'G0038_scan1.nii'
+    },
+    {
+      'image' : 'G0038_scan2.nii',
+      'boxes': 'G0038_scan2.nii'
+    },
+    {
+      'image' : 'G0039_scan1.nii',
+      'boxes': 'G0039_scan1.nii'
+    },
+    {
+      'image' : 'G0039_scan3.nii',
+      'boxes': 'G0039_scan3.nii'
+    },
+    {
+      'image' : 'G0040_scan1.nii',
+      'boxes': 'G0040_scan1.nii'
+    },
+    {
+      'image' : 'G0040_scan3.nii',
+      'boxes': 'G0040_scan3.nii'
+    },
+    {
+      'image' : 'G0041_scan2.nii',
+      'boxes': 'G0041_scan2.nii'
+    },
+    {
+      'image' : 'G0041_scan3.nii',
+      'boxes': 'G0041_scan3.nii'
+    },
+    {
+      'image' : 'G0042_scan1.nii',
+      'boxes': 'G0042_scan1.nii'
+    },
+    {
+      'image' : 'G0042_scan3.nii',
+      'boxes': 'G0042_scan3.nii'
+    },
+    {
+      'image' : 'G0043_scan1.nii',
+      'boxes': 'G0043_scan1.nii'
+    },
+    {
+      'image' : 'G0043_scan3.nii',
+      'boxes': 'G0043_scan3.nii'
+    },
+    {
+      'image' : 'G0044_scan1.nii',
+      'boxes': 'G0044_scan1.nii'
+    },
+    {
+      'image' : 'G0044_scan3.nii',
+      'boxes': 'G0044_scan3.nii'
+    },
+    {
+      'image' : 'G0045_scan1.nii',
+      'boxes': 'G0045_scan1.nii'
+    },
+    {
+      'image' : 'G0045_scan3.nii',
+      'boxes': 'G0045_scan3.nii'
+    },
+    {
+      'image' : 'G0046_scan1.nii',
+      'boxes': 'G0046_scan1.nii'
+    },
+    {
+      'image' : 'G0046_scan3.nii',
+      'boxes': 'G0046_scan3.nii'
+    },
+    {
+      'image' : 'G0047_scan1.nii',
+      'boxes': 'G0047_scan1.nii'
+    },
+    {
+      'image' : 'G0048_scan1.nii',
+      'boxes': 'G0048_scan1.nii'
+    },
+    {
+      'image' : 'G0049_scan1.nii',
+      'boxes': 'G0049_scan1.nii'
+    },
+    {
+      'image' : 'G0049_scan3.nii',
+      'boxes': 'G0049_scan3.nii'
+    },
+    {
+      'image' : 'G0050_scan1.nii',
+      'boxes': 'G0050_scan1.nii'
+    },
+    {
+      'image' : 'G0050_scan3.nii',
+      'boxes': 'G0050_scan3.nii'
+    },
+    {
+      'image' : 'G0051_scan1.nii',
+      'boxes': 'G0051_scan1.nii'
+    },
+    {
+      'image' : 'G0051_scan3.nii',
+      'boxes': 'G0051_scan3.nii'
+    },
+    {
+      'image' : 'G0052_scan1.nii',
+      'boxes': 'G0052_scan1.nii'
+    },
+    {
+      'image' : 'G0052_scan3.nii',
+      'boxes': 'G0052_scan3.nii'
+    },
+    {
+      'image' : 'G0053_scan2.nii',
+      'boxes': 'G0053_scan2.nii'
+    },
+    {
+      'image' : 'G0053_scan3.nii',
+      'boxes': 'G0053_scan3.nii'
+    },
+    {
+      'image' : 'G0054_scan1.nii',
+      'boxes': 'G0054_scan1.nii'
+    },
+    {
+      'image' : 'G0054_scan2.nii',
+      'boxes': 'G0054_scan2.nii'
+    },
+    {
+      'image' : 'G0055_scan1.nii',
+      'boxes': 'G0055_scan1.nii'
+    },
+    {
+      'image' : 'G0055_scan3.nii',
+      'boxes': 'G0055_scan3.nii'
+    },
+    {
+      'image' : 'G0056_scan1.nii',
+      'boxes': 'G0056_scan1.nii'
+    },
+    {
+      'image' : 'G0056_scan3.nii',
+      'boxes': 'G0056_scan3.nii'
+    },
+    {
+      'image' : 'G0057_scan1.nii',
+      'boxes': 'G0057_scan1.nii'
+    },
+    {
+      'image' : 'G0057_scan3.nii',
+      'boxes': 'G0057_scan3.nii'
+    },
+    {
+      'image' : 'G0058_scan1.nii',
+      'boxes': 'G0058_scan1.nii'
+    },
+    {
+      'image' : 'G0058_scan3.nii',
+      'boxes': 'G0058_scan3.nii'
+    },
+    {
+      'image' : 'G0059_scan1.nii',
+      'boxes': 'G0059_scan1.nii'
+    },
+    {
+      'image' : 'G0059_scan2.nii',
+      'boxes': 'G0059_scan2.nii'
+    },
+    {
+      'image' : 'G0060_scan1.nii',
+      'boxes': 'G0060_scan1.nii'
+    },
+    {
+      'image' : 'G0060_scan2.nii',
+      'boxes': 'G0060_scan2.nii'
+    },
+    {
+      'image' : 'G0062_scan1.nii',
+      'boxes': 'G0062_scan1.nii'
+    },
+    {
+      'image' : 'G0062_scan3.nii',
+      'boxes': 'G0062_scan3.nii'
+    },
+
+]

training/Resources/Malvik/inference.yaml

@@ -0,0 +1,23 @@
+device: 'cuda'
+
+data:
+  dataset: "/home/tumor/LungTumorSegmentation/Resources/Malvik/msd_dataset.yaml"
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 256
+    d_1: 256
+    d_2: 128
+
+model:
+  architecture: UNet4
+  loss: DiceLoss
+  weights: "/home/tumor/out/models/model_best.pth"
+  filter_base: 16
+  filter_expansion: 2
+  optimizer:
+    name: 'Adam'
+    lr: 0.0001
+
+inference:
+  save_directory: "/home/tumor/segs/"
+

training/Resources/Malvik/inference_data.yaml

@@ -0,0 +1,2533 @@
+image_prefix: "/home/tumor/data/LungDx-Tumor/Images/"
+boxes_prefix: "/home/tumor/data/LungDx-Tumor/Boxes/"
+test:
+  [
+    {
+      'image' : 'A0028_scan2.nii.gz',
+      'boxes': 'A0028_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0222_scan2.nii.gz',
+      'boxes': 'A0222_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0231_scan1.nii.gz',
+      'boxes': 'A0231_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0155_scan1.nii.gz',
+      'boxes': 'A0155_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0113_scan2.nii.gz',
+      'boxes': 'A0113_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0134_scan5.nii.gz',
+      'boxes': 'A0134_scan5.nii.gz'
+    },
+    {
+      'image' : 'A0261_scan2.nii.gz',
+      'boxes': 'A0261_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0078_scan6.nii.gz',
+      'boxes': 'A0078_scan6.nii.gz'
+    },
+    {
+      'image' : 'G0050_scan2.nii.gz',
+      'boxes': 'G0050_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0009_scan2.nii.gz',
+      'boxes': 'A0009_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0022_scan2.nii.gz',
+      'boxes': 'A0022_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0168_scan1.nii.gz',
+      'boxes': 'A0168_scan1.nii.gz'
+    },
+    {
+      'image' : 'B0022_scan2.nii.gz',
+      'boxes': 'B0022_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0063_scan3.nii.gz',
+      'boxes': 'A0063_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0136_scan1.nii.gz',
+      'boxes': 'A0136_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0056_scan2.nii.gz',
+      'boxes': 'G0056_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0102_scan1.nii.gz',
+      'boxes': 'A0102_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0161_scan1.nii.gz',
+      'boxes': 'A0161_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0260_scan2.nii.gz',
+      'boxes': 'A0260_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0086_scan1.nii.gz',
+      'boxes': 'A0086_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0121_scan2.nii.gz',
+      'boxes': 'A0121_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0166_scan1.nii.gz',
+      'boxes': 'A0166_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0049_scan2.nii.gz',
+      'boxes': 'G0049_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0147_scan2.nii.gz',
+      'boxes': 'A0147_scan2.nii.gz'
+    },
+    {
+      'image' : 'B0022_scan1.nii.gz',
+      'boxes': 'B0022_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0248_scan2.nii.gz',
+      'boxes': 'A0248_scan2.nii.gz'
+    },
+    {
+      'image' : 'B0024_scan2.nii.gz',
+      'boxes': 'B0024_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0052_scan1.nii.gz',
+      'boxes': 'G0052_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0112_scan3.nii.gz',
+      'boxes': 'A0112_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0029_scan3.nii.gz',
+      'boxes': 'A0029_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0063_scan2.nii.gz',
+      'boxes': 'A0063_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0052_scan2.nii.gz',
+      'boxes': 'G0052_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0041_scan1.nii.gz',
+      'boxes': 'A0041_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0019_scan2.nii.gz',
+      'boxes': 'G0019_scan2.nii.gz'
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+      'image' : 'A0160_scan1.nii.gz',
+      'boxes': 'A0160_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0165_scan1.nii.gz',
+      'boxes': 'A0165_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0105_scan3.nii.gz',
+      'boxes': 'A0105_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0221_scan1.nii.gz',
+      'boxes': 'A0221_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0206_scan2.nii.gz',
+      'boxes': 'A0206_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0253_scan2.nii.gz',
+      'boxes': 'A0253_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0106_scan4.nii.gz',
+      'boxes': 'A0106_scan4.nii.gz'
+    },
+    {
+      'image' : 'A0243_scan2.nii.gz',
+      'boxes': 'A0243_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0080_scan3.nii.gz',
+      'boxes': 'A0080_scan3.nii.gz'
+    },
+    {
+      'image' : 'B0043_scan2.nii.gz',
+      'boxes': 'B0043_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0062_scan2.nii.gz',
+      'boxes': 'G0062_scan2.nii.gz'
+    },
+    {
+      'image' : 'B0031_scan1.nii.gz',
+      'boxes': 'B0031_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0047_scan1.nii.gz',
+      'boxes': 'G0047_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0105_scan5.nii.gz',
+      'boxes': 'A0105_scan5.nii.gz'
+    },
+    {
+      'image' : 'A0113_scan4.nii.gz',
+      'boxes': 'A0113_scan4.nii.gz'
+    },
+    {
+      'image' : 'G0037_scan1.nii.gz',
+      'boxes': 'G0037_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0042_scan2.nii.gz',
+      'boxes': 'G0042_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0014_scan1.nii.gz',
+      'boxes': 'A0014_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0120_scan1.nii.gz',
+      'boxes': 'A0120_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0089_scan1.nii.gz',
+      'boxes': 'A0089_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0051_scan1.nii.gz',
+      'boxes': 'G0051_scan1.nii.gz'
+    },
+    {
+      'image' : 'B0027_scan1.nii.gz',
+      'boxes': 'B0027_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0087_scan3.nii.gz',
+      'boxes': 'A0087_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0019_scan2.nii.gz',
+      'boxes': 'A0019_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0216_scan1.nii.gz',
+      'boxes': 'A0216_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0043_scan1.nii.gz',
+      'boxes': 'A0043_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0157_scan1.nii.gz',
+      'boxes': 'A0157_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0008_scan2.nii.gz',
+      'boxes': 'G0008_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0109_scan2.nii.gz',
+      'boxes': 'A0109_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0145_scan1.nii.gz',
+      'boxes': 'A0145_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0073_scan2.nii.gz',
+      'boxes': 'A0073_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0241_scan2.nii.gz',
+      'boxes': 'A0241_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0173_scan1.nii.gz',
+      'boxes': 'A0173_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0250_scan2.nii.gz',
+      'boxes': 'A0250_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0040_scan2.nii.gz',
+      'boxes': 'G0040_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0242_scan2.nii.gz',
+      'boxes': 'A0242_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0184_scan2.nii.gz',
+      'boxes': 'A0184_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0155_scan2.nii.gz',
+      'boxes': 'A0155_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0041_scan2.nii.gz',
+      'boxes': 'A0041_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0181_scan2.nii.gz',
+      'boxes': 'A0181_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0011_scan2.nii.gz',
+      'boxes': 'A0011_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0129_scan3.nii.gz',
+      'boxes': 'A0129_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0254_scan1.nii.gz',
+      'boxes': 'A0254_scan1.nii.gz'
+    },
+    {
+      'image' : 'B0042_scan1.nii.gz',
+      'boxes': 'B0042_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0090_scan3.nii.gz',
+      'boxes': 'A0090_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0159_scan1.nii.gz',
+      'boxes': 'A0159_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0249_scan2.nii.gz',
+      'boxes': 'A0249_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0022_scan4.nii.gz',
+      'boxes': 'A0022_scan4.nii.gz'
+    },
+    {
+      'image' : 'A0261_scan1.nii.gz',
+      'boxes': 'A0261_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0183_scan2.nii.gz',
+      'boxes': 'A0183_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0005_scan1.nii.gz',
+      'boxes': 'A0005_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0248_scan1.nii.gz',
+      'boxes': 'A0248_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0153_scan1.nii.gz',
+      'boxes': 'A0153_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0250_scan1.nii.gz',
+      'boxes': 'A0250_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0159_scan2.nii.gz',
+      'boxes': 'A0159_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0125_scan1.nii.gz',
+      'boxes': 'A0125_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0052_scan2.nii.gz',
+      'boxes': 'A0052_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0093_scan2.nii.gz',
+      'boxes': 'A0093_scan2.nii.gz'
+    },
+    {
+      'image' : 'B0005_scan1.nii.gz',
+      'boxes': 'B0005_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0223_scan2.nii.gz',
+      'boxes': 'A0223_scan2.nii.gz'
+    },
+    {
+      'image' : 'B0021_scan1.nii.gz',
+      'boxes': 'B0021_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0177_scan2.nii.gz',
+      'boxes': 'A0177_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0024_scan1.nii.gz',
+      'boxes': 'G0024_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0034_scan2.nii.gz',
+      'boxes': 'G0034_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0113_scan3.nii.gz',
+      'boxes': 'A0113_scan3.nii.gz'
+    },
+    {
+      'image' : 'A0176_scan2.nii.gz',
+      'boxes': 'A0176_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0058_scan2.nii.gz',
+      'boxes': 'G0058_scan2.nii.gz'
+    },
+    {
+      'image' : 'B0018_scan2.nii.gz',
+      'boxes': 'B0018_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0227_scan2.nii.gz',
+      'boxes': 'A0227_scan2.nii.gz'
+    },
+    {
+      'image' : 'B0001_scan1.nii.gz',
+      'boxes': 'B0001_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0019_scan1.nii.gz',
+      'boxes': 'A0019_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0149_scan1.nii.gz',
+      'boxes': 'A0149_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0064_scan1.nii.gz',
+      'boxes': 'A0064_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0045_scan2.nii.gz',
+      'boxes': 'G0045_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0114_scan1.nii.gz',
+      'boxes': 'A0114_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0044_scan2.nii.gz',
+      'boxes': 'G0044_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0032_scan1.nii.gz',
+      'boxes': 'A0032_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0007_scan1.nii.gz',
+      'boxes': 'A0007_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0143_scan1.nii.gz',
+      'boxes': 'A0143_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0016_scan1.nii.gz',
+      'boxes': 'G0016_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0208_scan1.nii.gz',
+      'boxes': 'A0208_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0163_scan1.nii.gz',
+      'boxes': 'A0163_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0148_scan1.nii.gz',
+      'boxes': 'A0148_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0221_scan2.nii.gz',
+      'boxes': 'A0221_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0190_scan2.nii.gz',
+      'boxes': 'A0190_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0239_scan2.nii.gz',
+      'boxes': 'A0239_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0210_scan2.nii.gz',
+      'boxes': 'A0210_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0058_scan1.nii.gz',
+      'boxes': 'G0058_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0085_scan2.nii.gz',
+      'boxes': 'A0085_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0241_scan1.nii.gz',
+      'boxes': 'A0241_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0003_scan1.nii.gz',
+      'boxes': 'G0003_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0199_scan1.nii.gz',
+      'boxes': 'A0199_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0077_scan1.nii.gz',
+      'boxes': 'A0077_scan1.nii.gz'
+    },
+    {
+      'image' : 'B0040_scan2.nii.gz',
+      'boxes': 'B0040_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0224_scan2.nii.gz',
+      'boxes': 'A0224_scan2.nii.gz'
+    },
+    {
+      'image' : 'G0001_scan1.nii.gz',
+      'boxes': 'G0001_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0036_scan1.nii.gz',
+      'boxes': 'A0036_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0169_scan1.nii.gz',
+      'boxes': 'A0169_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0027_scan1.nii.gz',
+      'boxes': 'G0027_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0087_scan5.nii.gz',
+      'boxes': 'A0087_scan5.nii.gz'
+    },
+    {
+      'image' : 'A0259_scan1.nii.gz',
+      'boxes': 'A0259_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0001_scan1.nii.gz',
+      'boxes': 'A0001_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0142_scan1.nii.gz',
+      'boxes': 'A0142_scan1.nii.gz'
+    },
+    {
+      'image' : 'B0008_scan1.nii.gz',
+      'boxes': 'B0008_scan1.nii.gz'
+    },
+    {
+      'image' : 'G0057_scan1.nii.gz',
+      'boxes': 'G0057_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0154_scan2.nii.gz',
+      'boxes': 'A0154_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0135_scan1.nii.gz',
+      'boxes': 'A0135_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0200_scan2.nii.gz',
+      'boxes': 'A0200_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0232_scan1.nii.gz',
+      'boxes': 'A0232_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0113_scan1.nii.gz',
+      'boxes': 'A0113_scan1.nii.gz'
+    },
+    {
+      'image' : 'A0003_scan2.nii.gz',
+      'boxes': 'A0003_scan2.nii.gz'
+    },
+    {
+      'image' : 'A0199_scan2.nii.gz',
+      'boxes': 'A0199_scan2.nii.gz'
+    }
+]

training/Resources/Malvik/lungdx_dataset.yaml

@@ -0,0 +1,2569 @@
+image_prefix: "/home/tumor/data/LungDx-Lung/Images/"
+label_prefix: "/home/tumor/data/LungDx-Lung/Labels/"
+train:
+  [
+    {
+      'image' : 'A0001_scan1_left.nii.gz',
+      'label': 'A0001_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0001_scan1_right.nii.gz',
+      'label': 'A0001_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0001_scan2_left.nii.gz',
+      'label': 'A0001_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0002_scan1_right.nii.gz',
+      'label': 'A0002_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0002_scan2_left.nii.gz',
+      'label': 'A0002_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0002_scan2_right.nii.gz',
+      'label': 'A0002_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0003_scan1_left.nii.gz',
+      'label': 'A0003_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0003_scan2_left.nii.gz',
+      'label': 'A0003_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0004_scan1_right.nii.gz',
+      'label': 'A0004_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0004_scan2_right.nii.gz',
+      'label': 'A0004_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0005_scan1_right.nii.gz',
+      'label': 'A0005_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0005_scan2_right.nii.gz',
+      'label': 'A0005_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0006_scan1_right.nii.gz',
+      'label': 'A0006_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0006_scan4_right.nii.gz',
+      'label': 'A0006_scan4_right.nii.gz'
+    },
+    {
+      'image' : 'A0007_scan1_left.nii.gz',
+      'label': 'A0007_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0007_scan2_left.nii.gz',
+      'label': 'A0007_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0008_scan1_right.nii.gz',
+      'label': 'A0008_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0008_scan2_right.nii.gz',
+      'label': 'A0008_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0009_scan1_right.nii.gz',
+      'label': 'A0009_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0009_scan2_right.nii.gz',
+      'label': 'A0009_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0010_scan1_right.nii.gz',
+      'label': 'A0010_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0010_scan2_right.nii.gz',
+      'label': 'A0010_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0011_scan1_left.nii.gz',
+      'label': 'A0011_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0011_scan2_left.nii.gz',
+      'label': 'A0011_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0013_scan1_right.nii.gz',
+      'label': 'A0013_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0014_scan1_right.nii.gz',
+      'label': 'A0014_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0015_scan1_left.nii.gz',
+      'label': 'A0015_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0015_scan2_left.nii.gz',
+      'label': 'A0015_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0018_scan1_right.nii.gz',
+      'label': 'A0018_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0019_scan1_left.nii.gz',
+      'label': 'A0019_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0019_scan2_left.nii.gz',
+      'label': 'A0019_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0019_scan3_left.nii.gz',
+      'label': 'A0019_scan3_left.nii.gz'
+    },
+    {
+      'image' : 'A0022_scan1_right.nii.gz',
+      'label': 'A0022_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0022_scan2_right.nii.gz',
+      'label': 'A0022_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0022_scan3_right.nii.gz',
+      'label': 'A0022_scan3_right.nii.gz'
+    },
+    {
+      'image' : 'A0022_scan4_right.nii.gz',
+      'label': 'A0022_scan4_right.nii.gz'
+    },
+    {
+      'image' : 'A0023_scan1_left.nii.gz',
+      'label': 'A0023_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0026_scan1_left.nii.gz',
+      'label': 'A0026_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0026_scan2_left.nii.gz',
+      'label': 'A0026_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0028_scan1_right.nii.gz',
+      'label': 'A0028_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0028_scan2_right.nii.gz',
+      'label': 'A0028_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0029_scan1_right.nii.gz',
+      'label': 'A0029_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0029_scan2_right.nii.gz',
+      'label': 'A0029_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0029_scan3_right.nii.gz',
+      'label': 'A0029_scan3_right.nii.gz'
+    },
+    {
+      'image' : 'A0029_scan4_right.nii.gz',
+      'label': 'A0029_scan4_right.nii.gz'
+    },
+    {
+      'image' : 'A0031_scan1_right.nii.gz',
+      'label': 'A0031_scan1_right.nii.gz'
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+      'label': 'G0003_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0004_scan1_left.nii.gz',
+      'label': 'G0004_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0004_scan1_right.nii.gz',
+      'label': 'G0004_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0005_scan1_left.nii.gz',
+      'label': 'G0005_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0005_scan1_right.nii.gz',
+      'label': 'G0005_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0005_scan2_left.nii.gz',
+      'label': 'G0005_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0005_scan2_right.nii.gz',
+      'label': 'G0005_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0006_scan1_left.nii.gz',
+      'label': 'G0006_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0007_scan1_right.nii.gz',
+      'label': 'G0007_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0007_scan2_right.nii.gz',
+      'label': 'G0007_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0008_scan1_left.nii.gz',
+      'label': 'G0008_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0008_scan2_left.nii.gz',
+      'label': 'G0008_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0009_scan1_right.nii.gz',
+      'label': 'G0009_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0009_scan2_right.nii.gz',
+      'label': 'G0009_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0011_scan1_left.nii.gz',
+      'label': 'G0011_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0012_scan1_right.nii.gz',
+      'label': 'G0012_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0013_scan1_right.nii.gz',
+      'label': 'G0013_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0014_scan1_left.nii.gz',
+      'label': 'G0014_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0014_scan1_right.nii.gz',
+      'label': 'G0014_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0014_scan2_left.nii.gz',
+      'label': 'G0014_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0014_scan2_right.nii.gz',
+      'label': 'G0014_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0016_scan1_right.nii.gz',
+      'label': 'G0016_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0017_scan1_right.nii.gz',
+      'label': 'G0017_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0018_scan1_left.nii.gz',
+      'label': 'G0018_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0019_scan1_left.nii.gz',
+      'label': 'G0019_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0019_scan2_left.nii.gz',
+      'label': 'G0019_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0020_scan1_right.nii.gz',
+      'label': 'G0020_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0020_scan2_right.nii.gz',
+      'label': 'G0020_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0021_scan1_left.nii.gz',
+      'label': 'G0021_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0022_scan1_left.nii.gz',
+      'label': 'G0022_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0023_scan1_right.nii.gz',
+      'label': 'G0023_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0024_scan1_right.nii.gz',
+      'label': 'G0024_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0024_scan2_right.nii.gz',
+      'label': 'G0024_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0025_scan1_left.nii.gz',
+      'label': 'G0025_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0026_scan1_right.nii.gz',
+      'label': 'G0026_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0027_scan1_left.nii.gz',
+      'label': 'G0027_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0028_scan1_left.nii.gz',
+      'label': 'G0028_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0029_scan1_right.nii.gz',
+      'label': 'G0029_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0030_scan1_right.nii.gz',
+      'label': 'G0030_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0031_scan1_left.nii.gz',
+      'label': 'G0031_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0031_scan1_right.nii.gz',
+      'label': 'G0031_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0032_scan1_left.nii.gz',
+      'label': 'G0032_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0032_scan2_left.nii.gz',
+      'label': 'G0032_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0032_scan3_left.nii.gz',
+      'label': 'G0032_scan3_left.nii.gz'
+    },
+    {
+      'image' : 'G0032_scan4_left.nii.gz',
+      'label': 'G0032_scan4_left.nii.gz'
+    },
+    {
+      'image' : 'G0033_scan1_right.nii.gz',
+      'label': 'G0033_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0033_scan2_right.nii.gz',
+      'label': 'G0033_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0034_scan1_left.nii.gz',
+      'label': 'G0034_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0034_scan2_left.nii.gz',
+      'label': 'G0034_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0035_scan1_left.nii.gz',
+      'label': 'G0035_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0036_scan1_right.nii.gz',
+      'label': 'G0036_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0036_scan2_right.nii.gz',
+      'label': 'G0036_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0037_scan1_left.nii.gz',
+      'label': 'G0037_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0037_scan2_left.nii.gz',
+      'label': 'G0037_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0038_scan1_right.nii.gz',
+      'label': 'G0038_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0038_scan2_right.nii.gz',
+      'label': 'G0038_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0039_scan1_right.nii.gz',
+      'label': 'G0039_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0039_scan2_right.nii.gz',
+      'label': 'G0039_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0040_scan1_left.nii.gz',
+      'label': 'G0040_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0040_scan2_left.nii.gz',
+      'label': 'G0040_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0041_scan1_right.nii.gz',
+      'label': 'G0041_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0041_scan2_right.nii.gz',
+      'label': 'G0041_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0042_scan1_right.nii.gz',
+      'label': 'G0042_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0042_scan2_right.nii.gz',
+      'label': 'G0042_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0043_scan1_left.nii.gz',
+      'label': 'G0043_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0043_scan2_left.nii.gz',
+      'label': 'G0043_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0044_scan1_left.nii.gz',
+      'label': 'G0044_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0044_scan2_left.nii.gz',
+      'label': 'G0044_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0045_scan1_right.nii.gz',
+      'label': 'G0045_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0045_scan2_right.nii.gz',
+      'label': 'G0045_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0046_scan1_left.nii.gz',
+      'label': 'G0046_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0046_scan2_left.nii.gz',
+      'label': 'G0046_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0047_scan1_left.nii.gz',
+      'label': 'G0047_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0048_scan1_right.nii.gz',
+      'label': 'G0048_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0049_scan1_left.nii.gz',
+      'label': 'G0049_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0049_scan1_right.nii.gz',
+      'label': 'G0049_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0049_scan2_left.nii.gz',
+      'label': 'G0049_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0049_scan2_right.nii.gz',
+      'label': 'G0049_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0050_scan1_right.nii.gz',
+      'label': 'G0050_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0050_scan2_right.nii.gz',
+      'label': 'G0050_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0051_scan1_right.nii.gz',
+      'label': 'G0051_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0051_scan2_right.nii.gz',
+      'label': 'G0051_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0052_scan1_left.nii.gz',
+      'label': 'G0052_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0052_scan2_left.nii.gz',
+      'label': 'G0052_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0057_scan1_right.nii.gz',
+      'label': 'G0057_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0057_scan2_right.nii.gz',
+      'label': 'G0057_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0058_scan1_left.nii.gz',
+      'label': 'G0058_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0058_scan2_left.nii.gz',
+      'label': 'G0058_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0059_scan1_right.nii.gz',
+      'label': 'G0059_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0059_scan2_right.nii.gz',
+      'label': 'G0059_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0060_scan1_left.nii.gz',
+      'label': 'G0060_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0060_scan2_left.nii.gz',
+      'label': 'G0060_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'G0060_scan2_right.nii.gz',
+      'label': 'G0060_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0062_scan1_left.nii.gz',
+      'label': 'G0062_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0062_scan2_left.nii.gz',
+      'label': 'G0062_scan2_left.nii.gz'
+    }
+  ]
+
+val : 
+  [
+    {
+      'image' : 'G0055_scan1_right.nii.gz',
+      'label': 'G0055_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0055_scan2_right.nii.gz',
+      'label': 'G0055_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0056_scan1_right.nii.gz',
+      'label': 'G0056_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0056_scan2_right.nii.gz',
+      'label': 'G0056_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0141_scan1_left.nii.gz',
+      'label': 'A0141_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0141_scan2_left.nii.gz',
+      'label': 'A0141_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0142_scan1_left.nii.gz',
+      'label': 'A0142_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0143_scan1_right.nii.gz',
+      'label': 'A0143_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0144_scan1_right.nii.gz',
+      'label': 'A0144_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0144_scan2_right.nii.gz',
+      'label': 'A0144_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0145_scan1_left.nii.gz',
+      'label': 'A0145_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0145_scan2_left.nii.gz',
+      'label': 'A0145_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0146_scan1_right.nii.gz',
+      'label': 'A0146_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0146_scan2_right.nii.gz',
+      'label': 'A0146_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0147_scan1_right.nii.gz',
+      'label': 'A0147_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0147_scan2_right.nii.gz',
+      'label': 'A0147_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0148_scan1_right.nii.gz',
+      'label': 'A0148_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0148_scan2_right.nii.gz',
+      'label': 'A0148_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'B0033_scan2_left.nii.gz',
+      'label': 'B0033_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'B0034_scan1_left.nii.gz',
+      'label': 'B0034_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'B0034_scan2_left.nii.gz',
+      'label': 'B0034_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'B0036_scan1_left.nii.gz',
+      'label': 'B0036_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'B0036_scan2_left.nii.gz',
+      'label': 'B0036_scan2_left.nii.gz'
+    }
+  ]
+
+test :
+  [
+    {
+      'image' : 'A0201_scan1_right.nii.gz',
+      'label': 'A0201_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0201_scan2_right.nii.gz',
+      'label': 'A0201_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0202_scan1_right.nii.gz',
+      'label': 'A0202_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0202_scan2_right.nii.gz',
+      'label': 'A0202_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0203_scan1_left.nii.gz',
+      'label': 'A0203_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0203_scan2_left.nii.gz',
+      'label': 'A0203_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0204_scan1_left.nii.gz',
+      'label': 'A0204_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0204_scan2_left.nii.gz',
+      'label': 'A0204_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0205_scan1_left.nii.gz',
+      'label': 'A0205_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0205_scan2_left.nii.gz',
+      'label': 'A0205_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0206_scan1_left.nii.gz',
+      'label': 'A0206_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'A0206_scan2_left.nii.gz',
+      'label': 'A0206_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'A0208_scan1_right.nii.gz',
+      'label': 'A0208_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0208_scan2_right.nii.gz',
+      'label': 'A0208_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'A0210_scan1_right.nii.gz',
+      'label': 'A0210_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'A0210_scan2_right.nii.gz',
+      'label': 'A0210_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0053_scan1_right.nii.gz',
+      'label': 'G0053_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'G0053_scan2_right.nii.gz',
+      'label': 'G0053_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'G0054_scan1_left.nii.gz',
+      'label': 'G0054_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'G0054_scan2_left.nii.gz',
+      'label': 'G0054_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'B0025_scan1_right.nii.gz',
+      'label': 'B0025_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'B0026_scan1_left.nii.gz',
+      'label': 'B0026_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'B0026_scan2_left.nii.gz',
+      'label': 'B0026_scan2_left.nii.gz'
+    },
+    {
+      'image' : 'B0027_scan1_right.nii.gz',
+      'label': 'B0027_scan1_right.nii.gz'
+    },
+    {
+      'image' : 'B0027_scan2_right.nii.gz',
+      'label': 'B0027_scan2_right.nii.gz'
+    },
+    {
+      'image' : 'B0028_scan1_left.nii.gz',
+      'label': 'B0028_scan1_left.nii.gz'
+    },
+    {
+      'image' : 'B0028_scan2_left.nii.gz',
+      'label': 'B0028_scan2_left.nii.gz'
+    }
+  ]

training/Resources/Malvik/msd_dataset.yaml

@@ -0,0 +1,274 @@
+image_prefix: "/home/tumor/data/MSD-Lung/Images/"
+label_prefix: "/home/tumor/data/MSD-Lung/Labels/"
+
+train:
+  [
+    {
+      'image' : 'lung_001_right.nii.gz',
+      'label': 'lung_001_right.nii.gz'
+    },
+    {
+      'image' : 'lung_003_left.nii.gz',
+      'label': 'lung_003_left.nii.gz'
+    },
+    {
+      'image' : 'lung_004_left.nii.gz',
+      'label': 'lung_004_left.nii.gz'
+    },
+    {
+      'image' : 'lung_005_right.nii.gz',
+      'label': 'lung_005_right.nii.gz'
+    },
+    {
+      'image' : 'lung_006_left.nii.gz',
+      'label': 'lung_006_left.nii.gz'
+    },
+    {
+      'image' : 'lung_009_right.nii.gz',
+      'label': 'lung_009_right.nii.gz'
+    },
+    {
+      'image' : 'lung_010_left.nii.gz',
+      'label': 'lung_010_left.nii.gz'
+    },
+    {
+      'image' : 'lung_014_right.nii.gz',
+      'label': 'lung_014_right.nii.gz'
+    },
+    {
+      'image' : 'lung_015_right.nii.gz',
+      'label': 'lung_015_right.nii.gz'
+    },
+    {
+      'image' : 'lung_016_left.nii.gz',
+      'label': 'lung_016_left.nii.gz'
+    },
+    {
+      'image' : 'lung_018_right.nii.gz',
+      'label': 'lung_018_right.nii.gz'
+    },
+    {
+      'image' : 'lung_020_right.nii.gz',
+      'label': 'lung_020_right.nii.gz'
+    },
+    {
+      'image' : 'lung_022_right.nii.gz',
+      'label': 'lung_022_right.nii.gz'
+    },
+    {
+      'image' : 'lung_023_left.nii.gz',
+      'label': 'lung_023_left.nii.gz'
+    },
+    {
+      'image' : 'lung_023_right.nii.gz',
+      'label': 'lung_023_right.nii.gz'
+    },
+    {
+      'image' : 'lung_025_left.nii.gz',
+      'label': 'lung_025_left.nii.gz'
+    },
+    {
+      'image' : 'lung_026_right.nii.gz',
+      'label': 'lung_026_right.nii.gz'
+    },
+    {
+      'image' : 'lung_027_right.nii.gz',
+      'label': 'lung_027_right.nii.gz'
+    },
+    {
+      'image' : 'lung_028_left.nii.gz',
+      'label': 'lung_028_left.nii.gz'
+    },
+    {
+      'image' : 'lung_029_left.nii.gz',
+      'label': 'lung_029_left.nii.gz'
+    },
+    {
+      'image' : 'lung_031_right.nii.gz',
+      'label': 'lung_031_right.nii.gz'
+    },
+    {
+      'image' : 'lung_033_left.nii.gz',
+      'label': 'lung_033_left.nii.gz'
+    },
+    {
+      'image' : 'lung_034_right.nii.gz',
+      'label': 'lung_034_right.nii.gz'
+    },
+    {
+      'image' : 'lung_036_left.nii.gz',
+      'label': 'lung_036_left.nii.gz'
+    },
+    {
+      'image' : 'lung_037_right.nii.gz',
+      'label': 'lung_037_right.nii.gz'
+    },
+    {
+      'image' : 'lung_038_right.nii.gz',
+      'label': 'lung_038_right.nii.gz'
+    },
+    {
+      'image' : 'lung_041_right.nii.gz',
+      'label': 'lung_041_right.nii.gz'
+    },
+    {
+      'image' : 'lung_042_right.nii.gz',
+      'label': 'lung_042_right.nii.gz'
+    },
+    {
+      'image' : 'lung_043_right.nii.gz',
+      'label': 'lung_043_right.nii.gz'
+    },
+    {
+      'image' : 'lung_044_left.nii.gz',
+      'label': 'lung_044_left.nii.gz'
+    },
+    {
+      'image' : 'lung_045_left.nii.gz',
+      'label': 'lung_045_left.nii.gz'
+    },
+    {
+      'image' : 'lung_046_left.nii.gz',
+      'label': 'lung_046_left.nii.gz'
+    },
+    {
+      'image' : 'lung_047_right.nii.gz',
+      'label': 'lung_047_right.nii.gz'
+    },
+    {
+      'image' : 'lung_048_right.nii.gz',
+      'label': 'lung_048_right.nii.gz'
+    },
+    {
+      'image' : 'lung_049_right.nii.gz',
+      'label': 'lung_049_right.nii.gz'
+    },
+    {
+      'image' : 'lung_051_left.nii.gz',
+      'label': 'lung_051_left.nii.gz'
+    },
+    {
+      'image' : 'lung_053_right.nii.gz',
+      'label': 'lung_053_right.nii.gz'
+    },
+    {
+      'image' : 'lung_054_right.nii.gz',
+      'label': 'lung_054_right.nii.gz'
+    },
+    {
+      'image' : 'lung_055_left.nii.gz',
+      'label': 'lung_055_left.nii.gz'
+    },
+    {
+      'image' : 'lung_057_left.nii.gz',
+      'label': 'lung_057_left.nii.gz'
+    },
+    {
+      'image' : 'lung_058_right.nii.gz',
+      'label': 'lung_058_right.nii.gz'
+    },
+    {
+      'image' : 'lung_059_left.nii.gz',
+      'label': 'lung_059_left.nii.gz'
+    },
+    {
+      'image' : 'lung_061_left.nii.gz',
+      'label': 'lung_061_left.nii.gz'
+    },
+    {
+      'image' : 'lung_062_left.nii.gz',
+      'label': 'lung_062_left.nii.gz'
+    },
+    {
+      'image' : 'lung_064_left.nii.gz',
+      'label': 'lung_064_left.nii.gz'
+    },
+    {
+      'image' : 'lung_065_left.nii.gz',
+      'label': 'lung_065_left.nii.gz'
+    },
+    {
+      'image' : 'lung_066_left.nii.gz',
+      'label': 'lung_066_left.nii.gz'
+    },
+    {
+      'image' : 'lung_066_right.nii.gz',
+      'label': 'lung_066_right.nii.gz'
+    },
+    {
+      'image' : 'lung_069_right.nii.gz',
+      'label': 'lung_069_right.nii.gz'
+    },
+    {
+      'image' : 'lung_070_left.nii.gz',
+      'label': 'lung_070_left.nii.gz'
+    },
+    {
+      'image' : 'lung_071_right.nii.gz',
+      'label': 'lung_071_right.nii.gz'
+    },
+    {
+      'image' : 'lung_073_left.nii.gz',
+      'label': 'lung_073_left.nii.gz'
+    },
+    {
+      'image' : 'lung_074_left.nii.gz',
+      'label': 'lung_074_left.nii.gz'
+    },
+    
+]
+val : 
+  [
+    {
+      'image' : 'lung_075_left.nii.gz',
+      'label': 'lung_075_left.nii.gz'
+    },
+    {
+      'image' : 'lung_078_left.nii.gz',
+      'label': 'lung_078_left.nii.gz'
+    },
+    {
+      'image' : 'lung_079_right.nii.gz',
+      'label': 'lung_079_right.nii.gz'
+    },
+    {
+      'image' : 'lung_080_left.nii.gz',
+      'label': 'lung_080_left.nii.gz'
+    },
+    {
+      'image' : 'lung_081_right.nii.gz',
+      'label': 'lung_081_right.nii.gz'
+    }
+  ]
+
+test : 
+  [
+    {
+      'image' : 'lung_083_right.nii.gz',
+      'label': 'lung_083_right.nii.gz'
+    },
+    {
+      'image' : 'lung_084_left.nii.gz',
+      'label': 'lung_084_left.nii.gz'
+    },
+    {
+      'image' : 'lung_086_right.nii.gz',
+      'label': 'lung_086_right.nii.gz'
+    },
+    {
+      'image' : 'lung_092_right.nii.gz',
+      'label': 'lung_092_right.nii.gz'
+    },
+    {
+      'image' : 'lung_093_right.nii.gz',
+      'label': 'lung_093_right.nii.gz'
+    },
+    {
+      'image' : 'lung_095_right.nii.gz',
+      'label': 'lung_095_right.nii.gz'
+    },
+    {
+      'image' : 'lung_096_right.nii.gz',
+      'label': 'lung_096_right.nii.gz'
+    }
+  ]

training/Resources/Malvik/student.yaml

@@ -0,0 +1,28 @@
+device: 'cuda'
+
+data:
+  train_dataset: ""
+  aug_prob: 0.1
+  scale_dim:
+    d_0: 128
+    d_1: 128
+    d_2: 128
+
+model:
+  architecture: UNet4
+  loss: DiceLoss
+  filter_base: 16
+  filter_expansion: 2
+  weights:
+  optimizer:
+    name: 'Adam'
+    lr: 0.000025
+
+train:
+  max_epochs: 500
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/home/tumor/out/metric.csv"
+  save_frequency: 50
+  model_directory: "/home/tumor/out/models/"
+  batch_size: 10

training/Resources/Malvik/teacher.yaml

@@ -0,0 +1,29 @@
+device: 'cuda'
+
+data:
+  train_dataset: "/home/tumor/LungTumorSegmentation/Resources/Malvik/teacher_data.yaml"
+  aug_prob: 0.25
+  scale_dim:
+    d_0: 128
+    d_1: 128
+    d_2: 128
+
+model:
+  architecture: UNet5_Teacher
+  loss: DiceLoss
+  filter_base: 64
+  filter_expansion: 2
+  weights:
+  optimizer:
+    name: 'Adam'
+    lr: 0.000025
+
+train:
+  max_epochs: 500
+  val_frequency: 1
+  output_threshold: 0.5
+  metric_path: "/home/tumor/out/metric.csv"
+  save_frequency: 50
+  model_directory: "/home/tumor/out/models/"
+  batch_size: 10
+