xmindflow/SSCT
[ICCV 2023] Self-supervised Semantic Segmentation: Consistency over Transformation
This tool helps medical professionals, researchers, and radiologists accurately outline and segment anomalies like tumors, organs, or lesions in medical scans. You provide medical images, and it outputs precise segmented images, even when dealing with common variations like patient movement or different scan angles. It's designed for anyone needing to analyze medical imagery without extensive manual labeling.
No commits in the last 6 months.
Use this if you need to automatically and accurately segment specific areas within medical images, especially when labeled training data is scarce.
Not ideal if your primary goal is general image segmentation outside of medical contexts or if you have ample labeled data for supervised learning.
Stars
26
Forks
2
Language
Jupyter Notebook
License
MIT
Last pushed
Oct 11, 2025
Commits (30d)
0
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