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--- |
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license: cc-by-nc-nd-4.0 |
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language: |
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- en |
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tags: |
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- histology |
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- pathology |
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- vision |
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- pytorch |
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extra_gated_prompt: >- |
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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. |
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If you are a commercial entity, please contact the corresponding author. |
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extra_gated_fields: |
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Full name (first and last): text |
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Current affiliation (no abbreviations): text |
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Type of Affiliation: |
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type: select |
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options: |
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- Academia |
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- Industry |
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- label: Other |
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value: other |
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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 |
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Please explain your intended research use: text |
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I agree to all terms outlined above: checkbox |
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I agree to use this model for non-commercial, academic purposes only: checkbox |
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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 |
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metrics: |
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- accuracy |
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pipeline_tag: image-feature-extraction |
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library_name: timm |
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--- |
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# ♆ Patho-Bench |
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[📄 Preprint](https://arxiv.org/pdf/2502.06750) | [Code](https://github.com/mahmoodlab/Patho-Bench) |
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<img src="patho_bench_public.png" alt="Patho-Bench" style="width: 38%;" align="right"/> |
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**Patho-Bench is designed to evaluate patch and slide encoder foundation models for whole-slide images (WSIs).** |
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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. |
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This project was developed by the [Mahmood Lab](https://faisal.ai/) at Harvard Medical School and Brigham and Women's Hospital. This work was funded by NIH NIGMS R35GM138216. |
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> [!NOTE] |
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> 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. |
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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). |
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| **Family** | **Description** | **Tasks** | |
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|--------------------------------------|---------------------------------------------------------------------------------------|----------| |
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| **Morphological Subtyping** | Classifying distinct morphological patterns associated with different disease subtypes | 4 | |
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| **Tumor Grading** | Assigning a grade based on cellular differentiation and growth patterns | 2 | |
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| **Molecular Subtyping** | Predicting antigen presence (e.g., via IHC staining) | 3 | |
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| **Mutation Prediction** | Predicting specific genetic mutations in tumors | 21 | |
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| **Treatment Response & Assessment** | Evaluating patient response to treatment | 6 | |
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| **Survival Prediction** | Predicting survival outcomes and risk stratification | 6 | |
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## 🔥 Latest updates |
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- **February 2025**: Patho-Bench is now available on HuggingFace. |
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## ⚡ Installation |
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Install the required packages: |
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``` |
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pip install --upgrade datasets |
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pip install --upgrade huggingface_hub |
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``` |
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## 🔑 Authentication |
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```python |
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from huggingface_hub import login |
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login(token="YOUR_HUGGINGFACE_TOKEN") |
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``` |
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## ⬇️ Usage |
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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 TSV file containing the sample IDs, slide IDs, and labels. |
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> [!NOTE] |
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> 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). |
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### Download an individual task |
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```python |
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import datasets |
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dataset='cptac_coad' |
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task='KRAS_mutation' |
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datasets.load_dataset( |
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'MahmoodLab/Patho-Bench', |
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cache_dir='/path/to/saveto', |
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dataset_to_download=dataset, # Throws error if source not found |
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task_in_dataset=task, # Throws error if task not found in dataset |
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trust_remote_code=True |
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) |
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``` |
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### Download all tasks from a dataset |
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```python |
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import datasets |
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dataset='cptac_coad' |
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task='*' |
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datasets.load_dataset( |
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'MahmoodLab/Patho-Bench', |
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cache_dir='/path/to/saveto', |
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dataset_to_download=dataset, |
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task_in_dataset=task, |
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trust_remote_code=True |
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) |
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``` |
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### Download entire Patho-Bench [4.2 MB] |
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```python |
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import datasets |
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dataset='*' |
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datasets.load_dataset( |
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'MahmoodLab/Patho-Bench', |
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cache_dir='/path/to/saveto', |
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dataset_to_download=dataset, |
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trust_remote_code=True |
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) |
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``` |
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## 📢 Image data access links |
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For each dataset in Patho-Bench, please visit the respective repository below to download the raw image data. |
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| Dataset | Link | |
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|---------|------| |
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| EBRAINS [Roetzer et al., 2022] | [https://doi.org/10.25493/WQ48-ZGX](https://doi.org/10.25493/WQ48-ZGX) | |
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| BRACS [Brancati et al., 2021] | [https://www.bracs.icar.cnr.it/](https://www.bracs.icar.cnr.it/) | |
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| PANDA [Bulten et al., 2022] | [https://panda.grand-challenge.org/data/](https://panda.grand-challenge.org/data/) | |
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| IMP [Neto et al., 2024] | [https://rdm.inesctec.pt/dataset/nis-2023-008](https://rdm.inesctec.pt/dataset/nis-2023-008) | |
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| BCNB [Xu et al., 2021] | [https://bupt-ai-cz.github.io/BCNB/](https://bupt-ai-cz.github.io/BCNB/) | |
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| CPTAC-BRCA [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-brca/](https://www.cancerimagingarchive.net/collection/cptac-brca/) | |
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| CPTAC-CCRCC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-ccrcc/](https://www.cancerimagingarchive.net/collection/cptac-ccrcc/) | |
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| CPTAC-COAD [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-coad/](https://www.cancerimagingarchive.net/collection/cptac-coad/) | |
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| CPTAC-GBM [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-gbm/](https://www.cancerimagingarchive.net/collection/cptac-gbm/) | |
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| CPTAC-HNSC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-hnsc/](https://www.cancerimagingarchive.net/collection/cptac-hnsc/) | |
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| CPTAC-LSCC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-lscc/](https://www.cancerimagingarchive.net/collection/cptac-lscc/) | |
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| CPTAC-LUAD [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-luad/](https://www.cancerimagingarchive.net/collection/cptac-luad/) | |
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| CPTAC-PDAC [Edwards et al., 2015] | [https://www.cancerimagingarchive.net/collection/cptac-pda/](https://www.cancerimagingarchive.net/collection/cptac-pda/) | |
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| MUT-HET-RCC | [https://doi.org/10.25452/figshare.plus.c.5983795](https://doi.org/10.25452/figshare.plus.c.5983795) | |
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| OV-Bevacizumab [Wang et al., 2022] | [https://www.nature.com/articles/s41597-022-01127-6](https://www.nature.com/articles/s41597-022-01127-6) | |
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| 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) | |
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| POST-NAT-BRCA | [https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.23244](https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.23244) | |
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| BOEHMK | [https://www.synapse.org/Synapse:syn25946117/wiki/611576](https://www.synapse.org/Synapse:syn25946117/wiki/611576) | |
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| MBC | [https://www.synapse.org/Synapse:syn59490671/wiki/628046](https://www.synapse.org/Synapse:syn59490671/wiki/628046) | |
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| SURGEN | [https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1285](https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD1285) / [arXiv](https://arxiv.org/abs/2502.04946) | |
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## 📇 Contact |
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For any questions, contact: |
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- Faisal Mahmood ([email protected]) |
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- Anurag Vaidya ([email protected]) |
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- Andrew Zhang ([email protected]) |
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- Guillaume Jaume ([email protected]) |
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## 📜 Data description |
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Developed by: Mahmood Lab AI for Pathology @ Harvard/BWH |
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Repository: GitHub |
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License: CC-BY-NC-4.0 |
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## 🤝 Acknowledgements |
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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. |
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## 📰 How to cite |
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If Patho-Bench contributes to your research, please cite: |
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``` |
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@article{vaidya2025molecular, |
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title={Molecular-driven Foundation Model for Oncologic Pathology}, |
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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}, |
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journal={arXiv preprint arXiv:2501.16652}, |
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year={2025} |
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} |
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@article{zhang2025standardizing, |
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title={Accelerating Data Processing and Benchmarking of AI Models for Pathology}, |
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author={Zhang, Andrew and Jaume, Guillaume and Vaidya, Anurag and Ding, Tong and Mahmood, Faisal}, |
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journal={arXiv preprint arXiv:2502.06750}, |
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year={2025} |
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} |
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``` |
