---
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}
}
```