WEAKLY SUPERVISED IN-CONTEXT LEARNING-POWERED HIGH-EFFICIENCY UNIVERSAL MODELS FOR MEDICAL IMAGE SEGMENTATION
In medical image segmentation, universal models-including interactive and in-context learning (ICL) variants-deliver robust generalization capabilities but suffer from heavy reliance on extensive annotations. Interactive models depend on repeated user
Authors
- Naranhvwar Tvlg
Institutions
- Mongolian University of Science and Technology (MN)
Publication Details
- Published
- 2025-10-13
- DOI
- https://doi.org/10.22541/au.176038351.16722470/v1
- Primary Topic
- Medical Image Segmentation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00