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medseg-rai-pannuke
Devisri515/medseg-rai-pannuke
medseg-rai-pannuke is a image segmentation model from Devisri515. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as cc-by-nc-sa-4.0.
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 Pa…
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Updated Jun 10, 2026
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.pth196 MB · 100%
At a glance
- Task
- Image Segmentation
- Library
- pytorch
- License
- cc-by-nc-sa-4.0
- Access
- Public
- Created
- Jun 10, 2026
- Updated
- Jun 10, 2026
- SHA
- e279af30
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Model card
From the Hugging Face model README
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.
- Task
- Image Segmentation
- Library
- pytorch
- License
- cc-by-nc-sa-4.0
- Created
- Jun 10, 2026
- Updated
- Jun 10, 2026