AI RESEARCH
Topology-Driven Transferability Estimation of Medical Foundation Models for Segmentation
arXiv CS.AI
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ArXi:2602.23916v2 Announce Type: replace-cross The advent of large-scale self-supervised learning (SSL) has produced a vast zoo of medical foundation models. However, selecting optimal medical foundation models for specific segmentation tasks remains a computational bottleneck. Existing Transferability Estimation (TE) metrics, primarily designed for classification, rely on global statistical assumptions and fail to capture the topological complexity essential for dense prediction.