metadata
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 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, 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,
Zenodo 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:
@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}
}