| --- |
| license: other |
| license_name: cc-by-nc-sa-4.0-and-cc-by-nc-nd-4.0 |
| library_name: pytorch |
| tags: [computational-pathology, histopathology, quality-control, tissue-segmentation, colorectal-cancer] |
| --- |
| |
| # INSIGHT preprocessing weights (GrandQC + CRC tissue classifier) |
|
|
| Preprocessing weights used by the [HIMICO SDK](https://github.com/pkeller00/HIMICO-SDK) before feature extraction |
| (QC masks and tissue types). They download automatically when you run the pipeline. |
|
|
| | File | Model | Source and licence | |
| |---|---|---| |
| | `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 | |
| | `grandqc/qc_mpp{1,1.5,2}.safetensors` | GrandQC artefact segmentation (U-Net, EfficientNet-B0, 8 classes) | as above | |
| | `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 | |
|
|
| The GrandQC files are the official GrandQC weights ([Zenodo 14507273](https://zenodo.org/records/14507273), |
| [Zenodo 14041538](https://zenodo.org/records/14041538)) converted from pickled PyTorch models to `safetensors` |
| state dicts, without retraining or other changes. Load them with `segmentation_models_pytorch` |
| (`UnetPlusPlus` / `Unet`, `encoder_name="timm-efficientnet-b0"`, `encoder_weights=None`). |
|
|
| Non-commercial research use only. The INSIGHT survival model itself is not distributed; it runs on the HIMICO server |
| at Warwick with an API key (see the HIMICO SDK). If you use these weights, please cite GrandQC and INSIGHT: |
|
|
| ```bibtex |
| @article{weng2024grandqc, |
| title = {GrandQC: a comprehensive solution to quality control problem in digital pathology}, |
| author = {Weng, Zhilong and others}, |
| journal = {Nature Communications}, year = {2024} |
| } |
| @article{keller2025insight, |
| title = {INSIGHT: Spatially resolved survival modelling from routine histology crosslinked with molecular |
| profiling reveals prognostic epithelial-immune axes in stage II/III colorectal cancer}, |
| author = {Keller, Piotr and others and Minhas, Fayyaz}, |
| journal = {arXiv preprint arXiv:2512.22262}, year = {2025} |
| } |
| ``` |
|
|
