Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning

ArXi:2509.10784v3 Announce Type: replace-cross Medical vision foundation models remain limited in downstream tasks, particularly volumetric medical image segmentation. While fine-tuning on labeled target-domain data improves performance, existing approaches typically rely on randomly selected samples, which may fail to identify the most informative data and thus hinder adaptation. To address the limitations, we propose an Active Selective Semi-supervised Fine-tuning framework for efficient adaptation of Med-VFMs to generalize across volumetric medical image segmentation.