Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentation

TL;DR

This paper introduces C-Graph, a novel graph-based framework for cross-domain few-shot medical image segmentation that leverages structural consistency and contrastive learning to improve generalization and source domain accuracy.

Contribution

The paper proposes a new graph modeling approach with SPG layers, SMD mechanism, and CNC loss to enhance cross-domain segmentation performance and structural understanding.

Findings

Outperforms previous methods on multiple benchmarks.

Achieves state-of-the-art cross-domain segmentation accuracy.

Maintains strong source domain performance.

Abstract

Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multimodal analysis is required. However, existing methods typically filter out domain-specific information to improve generalization, which inadvertently limits cross-domain performance and degrades source-domain accuracy. To address this, we present Contrastive Graph Modeling (C-Graph), a framework that leverages the structural consistency of medical images as a reliable domain-transferable prior. We represent image features as graphs, with pixels as nodes and semantic affinities as edges. A Structural Prior Graph (SPG) layer is proposed to capture and transfer target-category node dependencies and enable global structure modeling through explicit node interactions. Building upon SPG layers, we introduce a Subgraph Matching Decoding (SMD) mechanism that exploits semantic relations among nodes to guide prediction. Furthermore, we design a Confusion-minimizing Node Contrast (CNC) loss to mitigate node ambiguity and subgraph heterogeneity by contrastively enhancing node discriminability in the graph space. Our method significantly outperforms prior CD-FSMIS approaches across multiple cross-domain benchmarks, achieving state-of-the-art performance while simultaneously preserving strong segmentation accuracy on the source domain.