Medseg Rai Pannuke

--- 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