GP-VM$\times$SMA: Benchmarking General-Purpose Vision Models and Specialized Architectures for 2D Medical Image Segmentation

תקציר מקורי באנגליתarXiv:2603.13044v2 Announce Type: replace-cross Abstract: Medical image segmentation (MIS) is a fundamental component of computer-assisted diagnosis and clinical decision support. Over the past decade, numerous architectures specifically tailored to medical imaging have emerged to address domain-specific challenges such as low contrast, small anatomical structures, and limited annotated data. In parallel, rapid progress in computer vision has produced highly capable general-purpose vision models (GP-VMs) originally designed for natural images. Despite their strong performance on standard vision benchmarks, their effectiveness for MIS remains insufficiently understood. In this controlled empirical study, we examine whether specialized medical segmentation architectures (SMAs) provide system