dewenzeng/positional_cl
code for paper Positional Contrastive Learning for Volumetric Medical Image Segmentation
This project helps radiologists and medical imaging researchers accurately segment organs and abnormalities in 3D medical scans like CT and MRI. It takes raw volumetric medical images (e.g., of the heart) as input and outputs detailed segmentations, even with limited labeled data. The primary users are medical imaging specialists and researchers who work with cardiac or other volumetric medical image analysis.
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Use this if you need to perform precise segmentation of structures within 3D medical images, especially when working with congenital heart disease (CHD) or similar cardiac datasets, and want to leverage contrastive learning for better performance.
Not ideal if your primary need is for object detection or classification in 2D images, or if you require real-time inference on highly constrained devices.
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Python
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Sep 11, 2024
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