Topology-Driven Transferability Estimation of Medical Foundation Models for Segmentation 事件

Topology-Driven Transferability Estimation of Medical Foundation Models for Segmentation arXiv:2602.23916v2 Announce Type: replace Abstract: 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 assumption