Add INSIGHT CRC tissue classifier; per-file licences
Browse files- README.md +8 -5
- crc_tissue/vit_b16.safetensors +3 -0
README.md
CHANGED
|
@@ -1,25 +1,28 @@
|
|
| 1 |
---
|
| 2 |
-
license:
|
|
|
|
| 3 |
library_name: pytorch
|
| 4 |
tags: [computational-pathology, histopathology, quality-control, tissue-segmentation, colorectal-cancer]
|
| 5 |
---
|
| 6 |
|
| 7 |
-
# INSIGHT preprocessing weights (GrandQC)
|
| 8 |
|
| 9 |
-
|
| 10 |
-
They download automatically when you run the pipeline.
|
| 11 |
|
| 12 |
| File | Model | Source and licence |
|
| 13 |
|---|---|---|
|
| 14 |
| `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 |
|
| 15 |
| `grandqc/qc_mpp{1,1.5,2}.safetensors` | GrandQC artefact segmentation (U-Net, EfficientNet-B0, 8 classes) | as above |
|
|
|
|
| 16 |
|
| 17 |
The GrandQC files are the official GrandQC weights ([Zenodo 14507273](https://zenodo.org/records/14507273),
|
| 18 |
[Zenodo 14041538](https://zenodo.org/records/14041538)) converted from pickled PyTorch models to `safetensors`
|
| 19 |
state dicts, without retraining or other changes. Load them with `segmentation_models_pytorch`
|
| 20 |
(`UnetPlusPlus` / `Unet`, `encoder_name="timm-efficientnet-b0"`, `encoder_weights=None`).
|
| 21 |
|
| 22 |
-
Non-commercial research use only. The INSIGHT
|
|
|
|
| 23 |
|
| 24 |
```bibtex
|
| 25 |
@article{weng2024grandqc,
|
|
|
|
| 1 |
---
|
| 2 |
+
license: other
|
| 3 |
+
license_name: cc-by-nc-sa-4.0-and-cc-by-nc-nd-4.0
|
| 4 |
library_name: pytorch
|
| 5 |
tags: [computational-pathology, histopathology, quality-control, tissue-segmentation, colorectal-cancer]
|
| 6 |
---
|
| 7 |
|
| 8 |
+
# INSIGHT preprocessing weights (GrandQC + CRC tissue classifier)
|
| 9 |
|
| 10 |
+
Preprocessing weights used by the [HIMICO SDK](https://github.com/pkeller00/HIMICO-SDK) before feature extraction
|
| 11 |
+
(QC masks and tissue types). They download automatically when you run the pipeline.
|
| 12 |
|
| 13 |
| File | Model | Source and licence |
|
| 14 |
|---|---|---|
|
| 15 |
| `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 |
|
| 16 |
| `grandqc/qc_mpp{1,1.5,2}.safetensors` | GrandQC artefact segmentation (U-Net, EfficientNet-B0, 8 classes) | as above |
|
| 17 |
+
| `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 |
|
| 18 |
|
| 19 |
The GrandQC files are the official GrandQC weights ([Zenodo 14507273](https://zenodo.org/records/14507273),
|
| 20 |
[Zenodo 14041538](https://zenodo.org/records/14041538)) converted from pickled PyTorch models to `safetensors`
|
| 21 |
state dicts, without retraining or other changes. Load them with `segmentation_models_pytorch`
|
| 22 |
(`UnetPlusPlus` / `Unet`, `encoder_name="timm-efficientnet-b0"`, `encoder_weights=None`).
|
| 23 |
|
| 24 |
+
Non-commercial research use only. The INSIGHT survival model itself is not distributed; it runs on the HIMICO server
|
| 25 |
+
at Warwick with an API key (see the HIMICO SDK). If you use these weights, please cite GrandQC and INSIGHT:
|
| 26 |
|
| 27 |
```bibtex
|
| 28 |
@article{weng2024grandqc,
|
crc_tissue/vit_b16.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c13051b569e40ac9e83628630cc001998c0e5004b74052cdf7640bde378b42c6
|
| 3 |
+
size 343233320
|