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Link paper and project page to model card

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Hi, I'm Niels from the community science team at Hugging Face.

This PR improves the documentation for AtlasPatch by:
- Linking the model card to the research paper on Hugging Face Papers.
- Adding a link to the official project page for better accessibility.
- Updating the citation section with the correct paper URL.
- Ensuring the `library_name` and metadata are correctly set for discoverability.

Files changed (1) hide show
  1. README.md +9 -5
README.md CHANGED
@@ -1,8 +1,8 @@
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  ---
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- license: cc-by-nc-sa-4.0
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  language:
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  - en
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  library_name: sam2
 
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  pipeline_tag: image-segmentation
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  tags:
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  - whole-slide-imaging
@@ -13,7 +13,11 @@ tags:
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  # AtlasPatch: Whole-Slide Image Tissue Segmentation
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- Segmentation model for whole-slide image (WSI) thumbnails, built on **Segment Anything 2 (SAM2) Tiny** and finetuned only on the normalization layers. The model takes a **power-based WSI thumbnail at 1.25x magnification level (resized to 1024×1024)** and predicts a binary tissue mask. Training used segmented thumbnails. AtlasPatch codebase (WSI preprocessing & tooling): https://github.com/AtlasAnalyticsLab/AtlasPatch
 
 
 
 
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  ## Quickstart
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@@ -89,8 +93,8 @@ If you use this model, please cite our paper:
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  @article{atlaspatch2025,
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  title = {AtlasPatch: An Efficient and Scalable Tool for Whole Slide Image Preprocessing in Computational Pathology},
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  author = {Alagha, Ahmed and Leclerc, Christopher and Kotp, Yousef and Abdelwahed, Omar and Moras, Calvin and Rentopoulos, Peter and Rostami, Rose and Nguyen, Bich Ngoc and Baig, Jumanah and Khellaf, Abdelhakim and Trinh, Vincent Quoc-Huy and Mizouni, Rabeb and Otrok, Hadi and Bentahar, Jamal and Hosseini, Mahdi S.},
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- journal = {arXiv},
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  year = {2025},
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- url = {TODO: coming soon}
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  }
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- ```
 
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  ---
 
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  language:
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  - en
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  library_name: sam2
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+ license: cc-by-nc-sa-4.0
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  pipeline_tag: image-segmentation
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  tags:
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  - whole-slide-imaging
 
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  # AtlasPatch: Whole-Slide Image Tissue Segmentation
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+ [Paper](https://huggingface.co/papers/2602.03998) | [Project Page](https://atlasanalyticslab.github.io/AtlasPatch/) | [GitHub](https://github.com/AtlasAnalyticsLab/AtlasPatch)
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+
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+ Segmentation model for whole-slide image (WSI) thumbnails, introduced in the paper [AtlasPatch: An Efficient and Scalable Tool for Whole Slide Image Preprocessing in Computational Pathology](https://huggingface.co/papers/2602.03998).
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+
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+ The model is built on **Segment Anything 2 (SAM2) Tiny** and finetuned only on the normalization layers. The model takes a **power-based WSI thumbnail at 1.25x magnification level (resized to 1024×1024)** and predicts a binary tissue mask. Training used segmented thumbnails.
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  ## Quickstart
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  @article{atlaspatch2025,
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  title = {AtlasPatch: An Efficient and Scalable Tool for Whole Slide Image Preprocessing in Computational Pathology},
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  author = {Alagha, Ahmed and Leclerc, Christopher and Kotp, Yousef and Abdelwahed, Omar and Moras, Calvin and Rentopoulos, Peter and Rostami, Rose and Nguyen, Bich Ngoc and Baig, Jumanah and Khellaf, Abdelhakim and Trinh, Vincent Quoc-Huy and Mizouni, Rabeb and Otrok, Hadi and Bentahar, Jamal and Hosseini, Mahdi S.},
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+ journal = {arXiv preprint arXiv:2602.03998},
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  year = {2025},
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+ url = {https://huggingface.co/papers/2602.03998}
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  }
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+ ```