YangTuanAnh/UNet-HoVerGNN
UNet-HoVerGNN: Structured Graph Integration into HoVerNet for Enhanced Nuclei Segmentation and Classification
This project helps pathology researchers accurately identify and categorize individual cell nuclei within microscope images. You input whole-slide pathology images, and it outputs precise segmentation masks for each nucleus and its specific classification (e.g., cancer type or cell type). This tool is for scientists, pathologists, and computational biologists working with histopathology image analysis.
Use this if you need a robust, accurate method to segment and classify cell nuclei in your digital pathology images, especially for multi-organ or pan-cancer datasets.
Not ideal if you are working with non-biological images or require a tool that does not involve deep learning setup and execution.
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Last pushed
Mar 04, 2026
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