| | --- |
| | license: cc-by-nc-nd-4.0 |
| | language: |
| | - en |
| | tags: |
| | - histology |
| | - pathology |
| | - vision |
| | - pytorch |
| | extra_gated_prompt: >- |
| | The data and associated code are released under the CC-BY-NC 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. |
| | If you are a commercial entity, please contact the corresponding author. |
| | extra_gated_fields: |
| | Full name (first and last): text |
| | Current affiliation (no abbreviations): text |
| | Type of Affiliation: |
| | type: select |
| | options: |
| | - Academia |
| | - Industry |
| | - label: Other |
| | value: other |
| | Current and official institutional email (**this must match your primary email in your Hugging Face account, @gmail/@hotmail/@qq email domains will be denied**): text |
| | Please explain your intended research use: text |
| | I agree to all terms outlined above: checkbox |
| | I agree to use this model for non-commercial, academic purposes only: checkbox |
| | I agree not to distribute the model, if another user within your organization wishes to use Patho-Bench data, they must register as an individual user: checkbox |
| | metrics: |
| | - accuracy |
| | pipeline_tag: image-feature-extraction |
| | library_name: timm |
| | --- |
| | |
| | # ♆ Patho-Bench |
| | [📄 Preprint](https://arxiv.org/pdf/2502.06750) | [Code](https://github.com/mahmoodlab/patho-bench) |
| |
|
| | <img src="patho_bench_public.png" alt="Patho-Bench" style="width: 38%;" align="right"/> |
| |
|
| | **Patho-Bench is designed for high-throughput evaluations of patch and slide encoder foundation models for whole-slide images (WSIs).** |
| |
|
| | This HuggingFace repository contains the data splits for the public Patho-Bench tasks. Please visit our codebase on [GitHub](https://github.com/mahmoodlab/patho-bench) for the full codebase and benchmark implementation. |
| |
|
| | This project was developed by the [Mahmood Lab](https://faisal.ai/) at Harvard Medical School and Brigham and Women's Hospital. |
| |
|
| | > [!NOTE] |
| | > Contributions are welcome! If you'd like to submit a new dataset and/or task for inclusion in Patho-Bench, please reach out to us via the [Issues](https://github.com/mahmoodlab/patho-bench/issues) tab of our Github repo. |
| |
|
| | Currently, Patho-Bench contains the following task families. We will add more tasks in the future. For further details on each task, please refer to the [THREADS foundation model paper](https://arxiv.org/abs/2501.16652). |
| |
|
| | | **Family** | **Description** | **Tasks** | |
| | |--------------------------------------|---------------------------------------------------------------------------------------|----------| |
| | | **Morphological Subtyping** | Classifying distinct morphological patterns associated with different disease subtypes | 4 | |
| | | **Tumor Grading** | Assigning a grade based on cellular differentiation and growth patterns | 2 | |
| | | **Molecular Subtyping** | Predicting antigen presence (e.g., via IHC staining) | 3 | |
| | | **Mutation Prediction** | Predicting specific genetic mutations in tumors | 21 | |
| | | **Treatment Response & Assessment** | Evaluating patient response to treatment | 6 | |
| | | **Survival Prediction** | Predicting survival outcomes and risk stratification | 6 | |
| |
|
| | ## 🔥 Latest updates |
| | - **January 2025**: Patho-Bench is now available on HuggingFace. |
| |
|
| | ## ⚡ Installation |
| | Install the required packages: |
| | ``` |
| | pip install --upgrade datasets |
| | pip install --upgrade huggingface_hub |
| | ``` |
| |
|
| | ## 🔑 Authentication |
| |
|
| | ```python |
| | from huggingface_hub import login |
| | login(token="YOUR_HUGGINGFACE_TOKEN") |
| | ``` |
| |
|
| | ## ⬇️ Usage |
| |
|
| | The Patho-Bench data splits are designed for use with the Patho-Bench [software package](https://github.com/mahmoodlab/patho-bench). However, you are welcome to use the data splits in your custom pipeline. Each task is associated with a YAML file containing task metadata and a CSV file containing the sample IDs, slide IDs, and labels. |
| |
|
| | > [!NOTE] |
| | > Patho-Bench only provides the data splits and labels, NOT the raw image data. You will need to download the raw image data from the respective dataset repositories (see links below). |
| |
|
| | ### Download an individual task |
| | ```python |
| | import datasets |
| | dataset='cptac_coad' |
| | task='KRAS_mutation' |
| | datasets.load_dataset( |
| | 'MahmoodLab/patho-bench', |
| | cache_dir='/path/to/saveto', |
| | dataset_to_download=dataset, # Throws error if source not found |
| | task_in_dataset=task, # Throws error if task not found in dataset |
| | trust_remote_code=True |
| | ) |
| | ``` |
| |
|
| | ### Download all tasks from a dataset |
| | ```python |
| | import datasets |
| | dataset='cptac_coad' |
| | task='*' |
| | datasets.load_dataset( |
| | 'MahmoodLab/patho-bench', |
| | cache_dir='/path/to/saveto', |
| | dataset_to_download=dataset, |
| | task_in_dataset=task, |
| | trust_remote_code=True |
| | ) |
| | ``` |
| |
|
| | ### Download entire Patho-Bench [4.2 MB] |
| | ```python |
| | import datasets |
| | dataset='*' |
| | datasets.load_dataset( |
| | 'MahmoodLab/patho-bench', |
| | cache_dir='/path/to/saveto', |
| | dataset_to_download=dataset, |
| | trust_remote_code=True |
| | ) |
| | ``` |
| |
|
| | ## 📢 Image data access links |
| |
|
| | For each dataset in Patho-Bench, please visit the respective repository below to download the raw image data. |
| |
|
| | | Dataset | Link | |
| | |---------|------| |
| | | EBRAINS [Roetzer et al., 2022] | [https://doi.org/10.25493/WQ48-ZGX](https://doi.org/10.25493/WQ48-ZGX) | |
| | | BRACS [Brancati et al., 2021] | [https://www.bracs.icar.cnr.it/](https://www.bracs.icar.cnr.it/) | |
| | | PANDA [Bulten et al., 2022] | [https://panda.grand-challenge.org/data/](https://panda.grand-challenge.org/data/) | |
| | | IMP [Neto et al., 2024] | [https://rdm.inesctec.pt/dataset/nis-2023-008](https://rdm.inesctec.pt/dataset/nis-2023-008) | |
| | | BCNB [Xu et al., 2021] | [https://bupt-ai-cz.github.io/BCNB/](https://bupt-ai-cz.github.io/BCNB/) | |
| | | CPTAC-BRCA [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-brca/](https://www.cancerimagingarchive.net/collection/cptac-brca/) | |
| | | CPTAC-CCRCC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-ccrcc/](https://www.cancerimagingarchive.net/collection/cptac-ccrcc/) | |
| | | CPTAC-COAD [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-coad/](https://www.cancerimagingarchive.net/collection/cptac-coad/) | |
| | | CPTAC-GBM [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-gbm/](https://www.cancerimagingarchive.net/collection/cptac-gbm/) | |
| | | CPTAC-HNSC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-hnsc/](https://www.cancerimagingarchive.net/collection/cptac-hnsc/) | |
| | | CPTAC-LSCC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-lscc/](https://www.cancerimagingarchive.net/collection/cptac-lscc/) | |
| | | CPTAC-LUAD [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-luad/](https://www.cancerimagingarchive.net/collection/cptac-luad/) | |
| | | CPTAC-PDAC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-pda/](https://www.cancerimagingarchive.net/collection/cptac-pda/) | |
| | | MUT-HET-RCC | [https://doi.org/10.25452/figshare.plus.c.5983795](https://doi.org/10.25452/figshare.plus.c.5983795) | |
| | | OV-Bevacizumab [Wang et al., 2022] | [https://www.nature.com/articles/s41597-022-01127-6](https://www.nature.com/articles/s41597-022-01127-6) | |
| | | NADT-Prostate [Wilkinson et al., 2021] | [https://www.medrxiv.org/content/10.1101/2020.09.29.20199711v1.full](https://www.medrxiv.org/content/10.1101/2020.09.29.20199711v1.full) | |
| | | POST-NAT-BRCA | [https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.23244](https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.23244) | |
| | | BOEHMK | [https://www.synapse.org/Synapse:syn25946117/wiki/611576](https://www.synapse.org/Synapse:syn25946117/wiki/611576) | |
| | | MBC | [https://www.synapse.org/Synapse:syn59490671/wiki/628046](https://www.synapse.org/Synapse:syn59490671/wiki/628046) | |
| | | SURGEN | [https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1285](https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1285) | |
| |
|
| | ## 📇 Contact |
| | For any questions, contact: |
| |
|
| | - Faisal Mahmood (faisalmahmood@bwh.harvard.edu) |
| | - Anurag Vaidya (avaidya@mit.edu) |
| | - Andrew Zhang (andrewzh@mit.edu) |
| | - Guillaume Jaume (gjaume@bwh.harvard.edu) |
| |
|
| | ## 📜 Data description |
| | Developed by: Mahmood Lab AI for Pathology @ Harvard/BWH |
| | Repository: GitHub |
| | License: CC-BY-NC-4.0 |
| |
|
| | ## 🤝 Acknowledgements |
| | Patho-Bench tasks were compiled from public image datasets and repositories (linked above). We thank the authors of these datasets for making their data publicly available. |
| |
|
| | ## 📰 How to cite |
| | If Patho-Bench contributes to your research, please cite: |
| |
|
| | ``` |
| | @article{vaidya2025molecular, |
| | title={Molecular-driven Foundation Model for Oncologic Pathology}, |
| | author={Vaidya, Anurag and Zhang, Andrew and Jaume, Guillaume and Song, Andrew H and Ding, Tong and Wagner, Sophia J and Lu, Ming Y and Doucet, Paul and Robertson, Harry and Almagro-Perez, Cristina and others}, |
| | journal={arXiv preprint arXiv:2501.16652}, |
| | year={2025} |
| | } |
| | |
| | @article{zhang2025standardizing, |
| | title={Accelerating Data Processing and Benchmarking of AI Models for Pathology}, |
| | author={Zhang, Andrew and Jaume, Guillaume and Vaidya, Anurag and Ding, Tong and Mahmood, Faisal}, |
| | journal={arXiv preprint arXiv:2502.06750}, |
| | year={2025} |
| | } |
| | ``` |
