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Add INSIGHT CRC tissue classifier; per-file licences

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  1. README.md +8 -5
  2. crc_tissue/vit_b16.safetensors +3 -0
README.md CHANGED
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  ---
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- license: cc-by-nc-sa-4.0
 
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  library_name: pytorch
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  tags: [computational-pathology, histopathology, quality-control, tissue-segmentation, colorectal-cancer]
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  ---
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- # INSIGHT preprocessing weights (GrandQC)
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- GrandQC weights used by the [`insight-crc`](https://github.com/pkeller00/INSIGHT) pipeline before feature extraction.
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- They download automatically when you run the pipeline.
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  | File | Model | Source and licence |
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  |---|---|---|
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  | `grandqc/td_mpp10.safetensors` | GrandQC tissue detection (U-Net++, EfficientNet-B0, 10 µm/px) | [GrandQC](https://github.com/cpath-ukk/grandqc), Weng et al., *Nat Commun* 2024. CC BY-NC-SA 4.0 |
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  | `grandqc/qc_mpp{1,1.5,2}.safetensors` | GrandQC artefact segmentation (U-Net, EfficientNet-B0, 8 classes) | as above |
 
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  The GrandQC files are the official GrandQC weights ([Zenodo 14507273](https://zenodo.org/records/14507273),
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  [Zenodo 14041538](https://zenodo.org/records/14041538)) converted from pickled PyTorch models to `safetensors`
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  state dicts, without retraining or other changes. Load them with `segmentation_models_pytorch`
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  (`UnetPlusPlus` / `Unet`, `encoder_name="timm-efficientnet-b0"`, `encoder_weights=None`).
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- Non-commercial research use only. The INSIGHT CRC tissue classifier is distributed with the gated INSIGHT weights at [pkeller01/INSIGHT](https://huggingface.co/pkeller01/INSIGHT). If you use these weights, please cite GrandQC and INSIGHT:
 
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  ```bibtex
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  @article{weng2024grandqc,
 
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  ---
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+ license: other
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+ license_name: cc-by-nc-sa-4.0-and-cc-by-nc-nd-4.0
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  library_name: pytorch
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  tags: [computational-pathology, histopathology, quality-control, tissue-segmentation, colorectal-cancer]
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  ---
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+ # INSIGHT preprocessing weights (GrandQC + CRC tissue classifier)
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+ Preprocessing weights used by the [HIMICO SDK](https://github.com/pkeller00/HIMICO-SDK) before feature extraction
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+ (QC masks and tissue types). They download automatically when you run the pipeline.
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  | File | Model | Source and licence |
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  |---|---|---|
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  | `grandqc/td_mpp10.safetensors` | GrandQC tissue detection (U-Net++, EfficientNet-B0, 10 µm/px) | [GrandQC](https://github.com/cpath-ukk/grandqc), Weng et al., *Nat Commun* 2024. CC BY-NC-SA 4.0 |
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  | `grandqc/qc_mpp{1,1.5,2}.safetensors` | GrandQC artefact segmentation (U-Net, EfficientNet-B0, 8 classes) | as above |
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+ | `crc_tissue/vit_b16.safetensors` | INSIGHT CRC tissue classifier (ViT-B/16, 224 px tiles at 0.5 µm/px, 8 classes: TUM NORM LYM MUS STR DEB MUC ADI) | INSIGHT (Keller et al. 2025). CC BY-NC-ND 4.0 |
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  The GrandQC files are the official GrandQC weights ([Zenodo 14507273](https://zenodo.org/records/14507273),
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  [Zenodo 14041538](https://zenodo.org/records/14041538)) converted from pickled PyTorch models to `safetensors`
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  state dicts, without retraining or other changes. Load them with `segmentation_models_pytorch`
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  (`UnetPlusPlus` / `Unet`, `encoder_name="timm-efficientnet-b0"`, `encoder_weights=None`).
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+ Non-commercial research use only. The INSIGHT survival model itself is not distributed; it runs on the HIMICO server
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+ at Warwick with an API key (see the HIMICO SDK). If you use these weights, please cite GrandQC and INSIGHT:
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  ```bibtex
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  @article{weng2024grandqc,
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