| --- |
| license: mit |
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| |
| # Checkpoint for Multistain Pretraining for Slide Representation Learning in Pathology (ECCV'24) |
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| Welcome to the official HuggingFace repository of the ECCV 2024 paper, "Multistain Pretraining for Slide Representation Learning in Pathology". |
| This project was developed at the [Mahmood Lab](https://faisal.ai/) at Harvard Medical School and Brigham and Women's Hospital. |
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| ## Model loging |
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| ``` |
| from huggingface_hub import login |
| login() |
| ``` |
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| You can refer [HuggingFace](https://huggingface.co/docs/huggingface_hub/en/quick-start#login-command) documentation for more details. |
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| ## Preprocessing: tissue segmentation, patching, and patch feature extraction |
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| We are extracting [CONCH](https://github.com/mahmoodlab/CONCH) features at 10x magnification on 256x256-pixel patches. Please refer to Madeleine public implementation to extract patch embeddings from a WSI. |
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| ## Extracting MADELEINE slide encoding |
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| You can obtain and run MADELEINE slide encoding (trained on Acrobat breast samples at 10x magnification) using: |
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| ``` |
| from core.models.factory import create_model_from_pretrained |
| |
| model, precision = create_model_from_pretrained('./models/') |
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| feats = load_h5('your_path_to_conch_patch_embeddings/XXX.h5') |
| |
| with torch.no_grad(): |
| with torch.amp.autocast(device_type="cuda", dtype=precision): |
| wsi_embed = model.encode_he(feats=feats, device='cuda') |
| ``` |
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