ArXiv

Geometry-aware Prototype Learning for Cross-domain Few-shot Medical Image Segmentation

2026-05-11 · 17:32 UTC ·Feifan Song, Yuntian Bo, Haofeng Zhang ·1 min read

Authors: Feifan Song, Yuntian Bo, Haofeng Zhang
Categories: cs.CV
arXiv: https://arxiv.org/abs/2605.10885v1
PDF: https://arxiv.org/pdf/2605.10885v1

Brief

GeoProto addresses cross-domain few-shot medical image segmentation by enriching prototypical matching with explicit geometric priors. The method's core, GAPE, augments appearance prototypes with learned ordinal offsets from an Ordinal Shape Branch trained under an ordinal-consistency loss (no extra labels beyond segmentation masks). According to the abstract, GeoProto attains state-of-the-art results across seven datasets and three settings (cross-modality, cross-sequence, cross-context). Only the paper abstract was available for this briefing.

Why it matters

GeoProto (Feifan Song, Yuntian Bo, Haofeng Zhang; arXiv:2605.10885v1, posted 2026-05-11) introduces Geometry-Aware Prototype Enrichment (GAPE) which augments local appearance prototypes with a learned geometric offset encoding an ordinal position within an organ's interior topology.

Key details

The geometric offset is produced by an auxiliary Ordinal Shape Branch (OSB) trained with an ordinally consistent objective that requires no annotations beyond standard segmentation masks; extensive experiments on seven datasets across three evaluation settings (cross-modality, cross-sequence, cross-context) report state-of-the-art performance.

Source evidence

Abstract

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. Building on this insight, we propose GeoProto, a geometry-aware CD-FSMIS framework that enriches prototypical matching with explicit structural priors. The core component, Geometry-Aware Prototype Enrichment (GAPE), augments each local appearance prototype with a learned geometric offset encoding its ordinal position within the organ's interior topology. This offset is derived from an auxiliary Ordinal Shape Branch (OSB) trained under an ordinally consistent objective that enforces monotonic variation of geometric embeddings across interior strata, requiring no annotation beyond standard segmentation masks. Extensive experiments across seven datasets spanning three evaluation settings (cross-modality, cross-sequence, and cross-context) demonstrate that GeoProto achieves state-of-the-art performance.