HoVer-Net
A PyTorch-based deep learning model for simultaneous nuclear instance segmentation and classification in histopathology images.
What is HoVer-Net?
HoVer-Net is a deep learning model for analyzing histopathology images, specifically designed to perform simultaneous nuclear instance segmentation and classification. It identifies individual nuclei in Hematoxylin and Eosin (H&E) stained tissue images and classifies them into types like epithelial, inflammatory, or spindle-shaped. This solves the problem of automated quantitative analysis in computational pathology, which is traditionally labor-intensive and subjective.
Researchers and developers in computational pathology, biomedical imaging, and medical AI who need to automate nuclear analysis in histology images. It's particularly useful for labs and institutions working on cancer diagnosis, tissue phenotyping, or digital pathology workflows.
Developers choose HoVer-Net because it unifies segmentation and classification into a single efficient network, reducing pipeline complexity. It offers state-of-the-art accuracy, supports whole-slide images, and provides pre-trained models for multiple public datasets, accelerating research and deployment in medical image analysis.
Overview
Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.
Use Cases
Best For
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
