Medseg Rai Pannuke
Trained weights (bestmodel.pth) for the MedSeg-RAI demo. U-Net++ with a ResNet-50 encoder, 6-class semantic segmentation of H&E nuclei: Background, Neoplastic, Inflammatory, Connective, Dead, Epithelial. Trained on...
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 187 MB
- Versión
- vBest model
- Actualizado
- 1 nov 2026
- Fuente
- Hugging Face
Descripción de la fuente
MedSeg-RAI: PanNuke nucleus segmentation (U-Net++ ResNet-50)
Trained weights (best_model.pth) for the MedSeg-RAI demo. U-Net++ with a ResNet-50
encoder, 6-class semantic segmentation of H&E nuclei: Background, Neoplastic,
Inflammatory, Connective, Dead, Epithelial. Trained on PanNuke folds 1 and 2.
Test results on PanNuke fold 3 (with test-time augmentation): mean foreground Dice 0.644, robust mean 0.714, pixel accuracy 0.919.
The checkpoint is a torch dict containing the state_dict, the run config, and the class
names. Load it with the project's medseg.evaluate.load_run.
The model was trained on PanNuke (CC BY-NC-SA 4.0), so these weights inherit the same non-commercial, share-alike terms. Not a medical device; research and portfolio use only.
Live demo and source: see the Hugging Face Space and the GitHub repository.
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 1 nov
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- license: cc-by-nc-sa-4.0 library_name: pytorch pipeline_tag: image-segmentation tags: - histopathology - medical-imaging - semantic-segmentation - pannuke - pytorch --- # MedSeg-RAI: PanNuke nucleus segmentation (U-Net++ ResNet-50) Trained weights (`best_model.pth`) for the MedSeg-RAI demo. U-Net++ with a ResNet-50 encoder, 6-class semantic segmentation of H&E nuclei: Background, Neoplastic, Inflammatory, Connective, Dead, Epithelial. Trained on PanNuke folds 1 and 2. Test results on PanNuke fold 3 (with test-time augmentation): mean foreground Dice 0.644, robust mean 0.714, pixel accuracy 0.919. The checkpoint is a torch dict containing the state_dict, the run config, and the class names. Load it with the project's `medseg.evaluate.load_run`. The model was trained on PanNuke (CC BY-NC-SA 4.0), so these weights inherit the same non-commercial, share-alike terms. Not a medical device; research and portfolio use only. Live demo and source: see the Hugging Face Space and the GitHub repository.
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library pytorch · Repo Devisri515/medseg-rai-pannuke
