xmindflow/SSL-contrastive
[ISBI 2024] Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
This project helps medical professionals and researchers automatically identify and outline specific organs or structures within 3D medical images like MRI and CT scans. It takes your clinical scans as input and outputs precise segmentations of anatomical regions, even with limited manually labeled data. This is ideal for radiologists, clinicians, and medical image analysts.
No commits in the last 6 months.
Use this if you need accurate 3D organ segmentation from clinical MRI or CT scans, especially when you have a lot of unlabeled data and only a small amount of expertly labeled examples.
Not ideal if you are working with non-medical images, 2D images, or if your primary goal is not segmentation.
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Language
Python
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Last pushed
Nov 23, 2023
Commits (30d)
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