[2602.23916] The Geometry of Transfer: Unlocking Medical Vision Manifolds for Training-Free Model Ranking

Computer Science > Computer Vision and Pattern Recognition arXiv:2602.23916 (cs) [Submitted on 27 Feb 2026] Title:The Geometry of Transfer: Unlocking Medical Vision Manifolds for Training-Free Model Ranking Authors:Jiaqi Tang, Shaoyang Zhang, Xiaoqi Wang, Jiaying Zhou, Yang Liu, Qingchao Chen View a PDF of the paper titled The Geometry of Transfer: Unlocking Medical Vision Manifolds for Training-Free Model Ranking, by Jiaqi Tang and 5 other authors View PDF HTML (experimental) 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 assumptions and fail to capture the topological complexity essential for dense prediction. We propose a novel Topology-Driven Transferability Estimation framework that evaluates manifold tractability rather than statistical overlap. Our approach introduces three components: (1) Global Representation Topology Divergence (GRTD), utilizing Minimum Spanning Trees to quantify feature-label structural isomorphism; (2) Local Boundary-Aware Topological Consistency (LBTC), which assesses manifold separability specifically at critical anatomical boundaries; and (3) Task-Adaptive Fusion, which dynamically integrates global and local metrics based on the semanti...