xmed-lab/GenericSSL
NeurIPS 2023: Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
This project helps medical professionals and researchers accurately segment structures in 3D medical images, like MRI or CT scans, even with limited labeled data. You provide a volumetric medical image dataset, and it outputs precise segmentations of anatomical regions. It's designed for medical imaging specialists, radiologists, and research scientists.
111 stars. No commits in the last 6 months.
Use this if you need to perform advanced segmentation on 3D medical scans across various settings (like semi-supervised learning, domain adaptation, or imbalanced datasets) and want a unified tool.
Not ideal if you are working with 2D images only or if your primary need is for fully supervised segmentation with abundant labeled data.
Stars
111
Forks
5
Language
Python
License
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Last pushed
Jun 04, 2024
Commits (30d)
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