Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision

ArXi:2603.13660v1 Announce Type: new Foundation models have transformed vision and language by learning general-purpose representations from large-scale unlabeled data, yet 3D medical imaging lacks analogous approaches. Existing self-supervised methods rely on low-level reconstruction or contrastive objectives that fail to capture the anatomical semantics critical for medical image analysis, limiting transfer to downstream tasks.