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README.md

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+---
+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/2501.16652) | [Code](https://github.com/mahmoodlab/patho-bench)
+
+<!-- <img src="benchmark.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 ([email protected])
+- Anurag Vaidya ([email protected])
+- Andrew Zhang ([email protected])
+- Guillaume Jaume ([email protected])
+
+## 📜 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}
+}
+```