Super-squash branch 'main' using huggingface_hub

Co-authored-by: asarmakeeva <[email protected]>

.gitattributes

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README.md

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+---
+license: cc0-1.0
+task_categories:
+- image-classification
+- image-segmentation
+tags:
+- medical
+pretty_name: T-SYNTH
+size_categories:
+- 1K<n<10K
+---
+# T-SYNTH
+
+<!-- Provide a quick summary of the dataset. -->
+
+T-SYNTH is a synthetic digital breast tomosynthesis (DBT) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://github.com/DIDSR/VICTRE) toolkit.
+
+## Dataset Details
+
+The dataset has the following characteristics:
+
+* Breast density: dense, heterogeneously dense, scattered, fatty
+* Mass radius (mm): 5.00, 7.00, 9.00
+* Mass density: 1.0, 1.06, 1.1 (ratio of mass radiodensity to that of fibroglandular tissue)
+
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+- **Curated by:** [Christopher Wiedeman](https://www.linkedin.com/in/christopher-wiedeman-a0b01014b), [Anastasiia Sarmakeeva](https://www.linkedin.com/in/anastasiia-sarmakeeva/), [Elena Sizikova](https://esizikova.github.io/), [Daniil Filienko](https://www.linkedin.com/in/daniil-filienko-800160215/), [Miguel Lago](https://www.linkedin.com/in/milaan/), [Jana Gut Delfino](https://www.linkedin.com/in/janadelfino/), [Aldo Badano](https://www.linkedin.com/in/aldobadano/)
+- **License:** Creative Commons 1.0 Universal License (CC0)
+
+## Data Acquisition Details
+
+**Imaging Modality:** Paired 2D digital mammography (DM) and 3D digital breast tomosynthesis (DBT) images. The DBT images are projected into C-VIEW via the method of (Klein, 2023). 
+
+**Manufacturer and Model:** Replica of the Siemens detector based on MC-GPU (Badal and Badano, 2009).
+
+**Demographics:** All breast phantoms are synthetic and do not represent real human subjects.
+
+**Cohort Description:** 9,000 synthetic digital breast tomosynthesis (DBT) samples, distributed across four breast density categories:
+
+| Breast Density        | Fatty     | Scattered | Hetero  | Dense   | **Total** |
+| --------- | --------- | --------- | ------- | ------- | --------- |
+| **Les.-free / Les.-present** | 1350/1350 | 1350/1350 | 900/900 | 900/900 | 4500/4500 |
+
+
+**Annotation Protocols:** Lesion masks and bounding boxes were generated automatically from the phantom.
+
+**Data Format and Structure:** Image files are in .raw format.
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Code:** [https://github.com/DIDSR/tsynth-release](https://github.com/DIDSR/tsynth-release)
+
+## Intended Use
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+T-SYNTH is intended to facilitate testing of AI with pre-computed synthetic digital breast tomosynthesis (DBT) data, complementing the M-SYNTH synthetic mammography dataset.
+
+
+## Ethical Considerations
+This work is using synthetically generated data and does not include any real patient-identifiable information. Publication of synthetic data aims to promote transparency, 
+reproducibility, and fairness in medical AI research. We recommend avoiding training models only on synthetic data without appropriate validation.
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+Directory layout:
+```
+T-SYNTH/data/
+├── cview
+├── embed_metadata
+├── pretrained_models
+├── results
+└── volumes_subset
+
+```
+Descriptions:
+
+* **`cview/`** -- contains T-SYNTH C-VIEW images.
+
+* **`embed_metadata/`** -- Configuration files needed to reproduce experiments.
+
+* **`pretrained_models/`**  -- Pretrained models for ```DBT```, ```DM``` and ```diffusion``` experiments to reproduce results from the paper. Note to reproduce you need files from **`embed_metadata/`**.
+
+* **`results/`** -- Output data and plots used in the paper (see [T-SYNTH repository](https://github.com/DIDSR/tsynth-release/tree/main/code/notebooks)). Description for each experiment could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md#experiment-configuration-map).
+
+* **`volumes_subset/`** -- example of volumetric data. The full data set will be released later due to volume.
+
+
+The data is organized by lesion size, breast density and lesion density.  Folder names follow the convention:
+   ```output_cview_det_Victre/device_data_VICTREPhantoms_spic_[LESION_DENSITY]/[BREAST_DENSITY]/2/[LESION_SIZE]/SIM.zip```.
+   
+Example path in `volumes_subset`:
+
+```
+device_data_VICTREPhantoms_spic_1.1/fatty/2/5.0/SIM/D2_5.0_fatty.1/1/
+├── reconstruction1.loc        # Lesion coordinates
+├── reconstruction1.mhd        # Metadata (raw format)
+├── reconstruction1.raw        # Raw image data
+└── reconstruction1_mask.h5    # Pixel-level segmentation masks for lesions and tissues
+```
+
+## How to use it
+
+The description how to use it could be found [here](https://github.com/DIDSR/tsynth-release/blob/main/code/README.md). 
+
+## Citation
+
+```
+@article{t-synth,
+  title={T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images},
+  author={Christopher Wiedeman, Anastasiia Sarmakeeva, Elena Sizikova, Daniil Filienko, Miguel Lago, Jana G. Delfino, Aldo Badano},
+  journal={},
+  volume={},
+  pages={},
+  year={2025}
+}
+```
+
+## Related Links
+1. [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://cdrh-rst.fda.gov/victre-silico-breast-imaging-pipeline).
+2. [M-SYNTH: A Dataset for the Comparative Evaluation of Mammography AI](https://cdrh-rst.fda.gov/m-synth-dataset-comparative-evaluation-mammography-ai).
+6. A. Kim*, N. Saharkhiz*, E. Sizikova*, M. Lago, B. Sahiner, J. G. Delfino, A. Badano. [S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images](https://github.com/DIDSR/ssynth-release). MICCAI 2024.
+4. [FDA Catalog of Regulatory Science Tools to Help Assess New Medical Devices](https://www.fda.gov/medical-devices/science-and-research-medical-devices/catalog-regulatory-science-tools-help-assess-new-medical-devices).

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+    csv_file: EMBED_split_full.csv
+    keyword: test
+  existing_paths: existing_Embed_fileset.pkl
+  sample_balance: balanced
+eval_dataset_name: EmbedDataset
+extra_columns_keys:
+- anon_dicom_path
+- breast_density
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+non_max_suppression_threshold: 0.25

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data/pretrained_models/DBT/add/real100_and_synth100/2/hyperparams.yaml

@@ -0,0 +1,13 @@
+batch_size: 16
+eval_dataset_kwargs:
+  data_split_dict:
+    csv_file: EMBED_split_full.csv
+    keyword: test
+  existing_paths: existing_Embed_fileset.pkl
+  sample_balance: balanced
+eval_dataset_name: EmbedDataset
+extra_columns_keys:
+- anon_dicom_path
+- breast_density
+model_path: /projects01/VICTRE/anastasii.sarmakeeva/msynth-scratch-chris/msynth-scratch/runs/DBT/add/real100_and_synth100/2/best_ckpt.pth
+non_max_suppression_threshold: 0.25

data/pretrained_models/DBT/add/real100_and_synth100/2/log1

@@ -0,0 +1 @@
+Saving results to /projects01/VICTRE/anastasii.sarmakeeva/msynth-scratch-chris/msynth-scratch/results/DBT/add/real100_and_synth100/2/result.csv.

data/pretrained_models/DBT/add/real100_and_synth100/3/hyperparams.yaml

@@ -0,0 +1,13 @@
+batch_size: 16
+eval_dataset_kwargs:
+  data_split_dict:
+    csv_file: EMBED_split_full.csv
+    keyword: test
+  existing_paths: existing_Embed_fileset.pkl
+  sample_balance: balanced
+eval_dataset_name: EmbedDataset
+extra_columns_keys:
+- anon_dicom_path
+- breast_density
+model_path: /projects01/VICTRE/anastasii.sarmakeeva/msynth-scratch-chris/msynth-scratch/runs/DBT/add/real100_and_synth100/3/best_ckpt.pth
+non_max_suppression_threshold: 0.25

data/pretrained_models/DBT/add/real100_and_synth100/3/log1

@@ -0,0 +1 @@
+Saving results to /projects01/VICTRE/anastasii.sarmakeeva/msynth-scratch-chris/msynth-scratch/results/DBT/add/real100_and_synth100/3/result.csv.

data/pretrained_models/DBT/add/real100_and_synth100/4/hyperparams.yaml

@@ -0,0 +1,13 @@
+batch_size: 16
+eval_dataset_kwargs:
+  data_split_dict:
+    csv_file: EMBED_split_full.csv
+    keyword: test
+  existing_paths: existing_Embed_fileset.pkl
+  sample_balance: balanced
+eval_dataset_name: EmbedDataset
+extra_columns_keys:
+- anon_dicom_path
+- breast_density
+model_path: /projects01/VICTRE/anastasii.sarmakeeva/msynth-scratch-chris/msynth-scratch/runs/DBT/add/real100_and_synth100/4/best_ckpt.pth
+non_max_suppression_threshold: 0.25

data/pretrained_models/DBT/add/real100_and_synth100/4/log1

@@ -0,0 +1 @@
+Saving results to /projects01/VICTRE/anastasii.sarmakeeva/msynth-scratch-chris/msynth-scratch/results/DBT/add/real100_and_synth100/4/result.csv.

data/pretrained_models/DBT/add/real100_and_synth100/4/result.csv

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+ [   0.  767.  160. 1145.]]","[[  17.431328  791.68744   146.92535   939.0955  ]
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data/pretrained_models/DBT/add/real100_and_synth100/5/hyperparams.yaml

@@ -0,0 +1,13 @@
+batch_size: 16
+eval_dataset_kwargs:
+  data_split_dict:
+    csv_file: EMBED_split_full.csv
+    keyword: test
+  existing_paths: existing_Embed_fileset.pkl
+  sample_balance: balanced
+eval_dataset_name: EmbedDataset
+extra_columns_keys:
+- anon_dicom_path
+- breast_density
+model_path: /projects01/VICTRE/anastasii.sarmakeeva/msynth-scratch-chris/msynth-scratch/runs/DBT/add/real100_and_synth100/5/best_ckpt.pth
+non_max_suppression_threshold: 0.25

data/pretrained_models/DBT/add/real100_and_synth100/5/log1

@@ -0,0 +1 @@
+Saving results to /projects01/VICTRE/anastasii.sarmakeeva/msynth-scratch-chris/msynth-scratch/results/DBT/add/real100_and_synth100/5/result.csv.