Model Overview
AttentionUNet for MRI Segmentation on the BraTS2023AdultGlioma dataset.
ATOMMIC: Training
To train, fine-tune, or test the model you will need to install ATOMMIC. We recommend you install it after you've installed latest Pytorch version.
pip install atommic['all']
How to Use this Model
The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
Corresponding configuration YAML files can be found here.
Automatically instantiate the model
pretrained: true
checkpoint: https://huggingface.co/wdika/SEG_UNet_BraTS2023AdultGlioma/blob/main/SEG_UNet_BraTS2023AdultGlioma.atommic
mode: test
Usage
You need to download the BraTS 2023 Adult Glioma dataset to effectively use this model. Check the BraTS2023AdultGlioma page for more information.
Model Architecture
model:
model_name: SEGMENTATIONUNET
segmentation_module: UNet
segmentation_module_input_channels: 4
segmentation_module_output_channels: 4
segmentation_module_channels: 32
segmentation_module_pooling_layers: 5
segmentation_module_dropout: 0.0
segmentation_module_normalize: false
segmentation_loss:
dice: 1.0
dice_loss_include_background: true # always set to true if the background is removed
dice_loss_to_onehot_y: false
dice_loss_sigmoid: false
dice_loss_softmax: false
dice_loss_other_act: none
dice_loss_squared_pred: false
dice_loss_jaccard: false
dice_loss_flatten: false
dice_loss_reduction: mean_batch
dice_loss_smooth_nr: 1e-5
dice_loss_smooth_dr: 1e-5
dice_loss_batch: true
dice_metric_include_background: true # always set to true if the background is removed
dice_metric_to_onehot_y: false
dice_metric_sigmoid: false
dice_metric_softmax: false
dice_metric_other_act: none
dice_metric_squared_pred: false
dice_metric_jaccard: false
dice_metric_flatten: false
dice_metric_reduction: mean_batch
dice_metric_smooth_nr: 1e-5
dice_metric_smooth_dr: 1e-5
dice_metric_batch: true
segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
segmentation_activation: sigmoid
magnitude_input: true
log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
normalization_type: minmax
normalize_segmentation_output: true
complex_data: false
Training
optim:
name: adam
lr: 1e-4
betas:
- 0.9
- 0.98
weight_decay: 0.0
sched:
name: InverseSquareRootAnnealing
min_lr: 0.0
last_epoch: -1
warmup_ratio: 0.1
trainer:
strategy: ddp
accelerator: gpu
devices: 1
num_nodes: 1
max_epochs: 10
precision: 16-mixed
enable_checkpointing: false
logger: false
log_every_n_steps: 50
check_val_every_n_epoch: -1
max_steps: -1
Performance
Evaluation can be performed using the segmentation evaluation script for the segmentation task, with --evaluation_type per_slice.
Results
Evaluation
DICE = 0.9372 +/- 0.1175 F1 = 0.6713 +/- 0.7867 HD95 = 3.504 +/- 2.089 IOU = 0.5346 +/- 0.6628
Limitations
This model was trained on the BraTS2023AdultGlioma dataset with stacked T1c, T1n, T2f, T2w images and might differ in performance compared to the leaderboard results.
References
[1] ATOMMIC
[2] Kazerooni AF, Khalili N, Liu X, et al. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs). 2023