AI RESEARCH
Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentation
arXiv CS.CV
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ArXi:2605.10885v1 Announce Type: new Cross-domain few-shot medical image segmentation (CD-FSMIS) requires a model to generalise simultaneously to novel anatomical categories and unseen imaging domains from only a handful of annotated examples. Existing prototypical approaches inevitably entangle anatomical structure with domain-specific appearance variations, and thus lack a stable reference for reliable matching under domain shift. We observe that the geometric structure of human anatomy constitutes a reliable, domain-transferable prior that has been overlooked.