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TEAM
TEAM is a PyTorch model release for computational pathology feature extraction and biomarker-driven downstream prediction. It contains a patch-level pathology encoder and a slide-level TEAM aggregation checkpoint for whole-slide-image-derived patch folders.
Code repository: https://github.com/juruoxcj/TEAM
Access
This repository is gated. Please request access on Hugging Face and provide your affiliation and intended research use. Access is manually reviewed by the authors.
Model Files
| File | Size | SHA256 | Description |
|---|---|---|---|
patch_weight.pth |
1157.31 MB | 1776e49a47bfb79fc2fd80aa09c88c4a6e1bcc7685a56b718ce51fbc04da127f |
Patch-level pathology encoder checkpoint. |
slide_weight.pth |
19.60 MB | 495758c03c73b660946ed6c0c7c4f52c9aa06577b2f25493ec4d5df61304be60 |
Slide-level TEAM aggregation checkpoint. |
Intended Use
TEAM is intended for research use in computational pathology workflows, including:
- extracting patch-level pathology embeddings from image tiles;
- aggregating patch features into slide-level TEAM representations;
- supporting downstream biomarker-driven prognostic stratification and therapeutic response prediction experiments when paired with the released source code.
TEAM is not intended for standalone clinical diagnosis, treatment selection, or deployment as a medical device.
Quick Start
Install the code from GitHub:
git clone https://github.com/juruoxcj/TEAM.git
cd TEAM
conda env create -f environment.yml
conda activate team
After your Hugging Face access request is approved, download the weights and run the demo:
python run_team.py \
--config ./configs/demo_config.json \
--patch_ckpt ./patch_weight.pth \
--slide_ckpt ./slide_weight.pth \
--device cuda
The included GitHub demo uses six unmodified TCGA BRCA patch images to demonstrate the expected input layout. On the tested CUDA workstation, the demo completed in about 21 seconds.
Inputs
TEAM expects one folder per slide, containing pre-extracted image patches:
slides/
`-- slide_A/
|-- patch_0001.png
|-- patch_0002.png
`-- ...
Supported image extensions are .png, .jpg, .jpeg, .tif, .tiff, .bmp, and .webp.
Optional de-identified clinical text can be provided as a JSON file mapping slide folder names to text. Do not include protected health information in filenames, metadata, or text fields.
Outputs
The upstream inference script writes a PyTorch .pt file containing:
| Key | Shape | Description |
|---|---|---|
patch_feat |
[N, 1024] |
Patch-level embeddings for N input patches. |
slide_feat |
[1, 512] |
Slide-level TEAM embedding. |
paths |
length N |
Input patch paths in feature order. |
Tested Environment
- Python 3.10.18
- PyTorch 2.7.1+cu126
- CUDA runtime 12.6
- NVIDIA RTX 5880 Ada Generation GPU
- Linux 5.15 x86_64
CPU execution is suitable for installation checks and downstream smoke tests. GPU execution is recommended for upstream feature extraction.
Training Data and Evaluation
TEAM was developed for pathology image analysis using whole-slide-image-derived patch data and associated study metadata described in the accompanying manuscript. Raw WSIs, patient-derived patch tiles, clinical records, patient-level annotations, and generated feature tensors are not redistributed in this model repository.
Quantitative evaluation should be interpreted through the accompanying manuscript and its Data Availability statement. The small public demo patches in the GitHub repository are provided only to demonstrate the input layout and inference workflow; they are not an evaluation dataset.
Limitations and Ethics
- The released checkpoints are for research use.
- Performance can vary across scanners, staining protocols, tissue preparation pipelines, and patient cohorts.
- Clinical deployment requires independent validation, governance review, and compliance with local regulations.
- Users are responsible for following data-use agreements and privacy requirements for controlled pathology data.
License
The model card and associated code release use the Apache License 2.0. Checkpoint use should follow this license and any applicable data-use restrictions described by the authors.
Citation
@article{mao2026interactive,
title={An Interactive Trustworthy AI Pathology Copilot to Improve Biomarker-Driven Prognostic Stratification and Therapeutic Response Prediction},
author={Mao, Yixiao and Xie, Chengjie and Li, Feng and Li, Danyi and Zhang, Wenyan and Zhang, Yidan and Li, Bingbing and Zhao, Chenglong and Zhang, Zhengyu and Tan, Ying and others},
journal={medRxiv},
pages={2026--05},
year={2026},
publisher={Cold Spring Harbor Laboratory Press}
}