AI RESEARCH
MedCore: Boundary-Preserving Medical Core Pruning for MedSAM
arXiv CS.LG
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ArXi:2605.13688v1 Announce Type: cross Medical segmentation foundation models such as SAM and MedSAM provide strong prompt-driven segmentation, but their image encoders are still too large for many clinical settings. Compression is also risky in medicine because a model can keep high Dice while losing boundary fidelity. We propose MedCore, a structured pruning framework for MedSAM. The main idea is to preserve two kinds of structures: structures that became important during SAM-to-MedSAM adaptation, and structures that have high boundary leverage.