Contrastive Graph Modeling for Few-Shot Medical Image Segmentation
Research Paper#Medical Image Segmentation, Few-Shot Learning, Graph Neural Networks🔬 Research|Analyzed: Jan 4, 2026 00:17•
Published: Dec 25, 2025 14:00
•1 min read
•ArXivAnalysis
This paper addresses the challenge of cross-domain few-shot medical image segmentation, a critical problem in medical applications where labeled data is scarce. The proposed Contrastive Graph Modeling (C-Graph) framework offers a novel approach by leveraging structural consistency in medical images. The key innovation lies in representing image features as graphs and employing techniques like Structural Prior Graph (SPG) layers, Subgraph Matching Decoding (SMD), and Confusion-minimizing Node Contrast (CNC) loss to improve performance. The paper's significance lies in its potential to improve segmentation accuracy in scenarios with limited labeled data and across different medical imaging domains.
Key Takeaways
- •Proposes Contrastive Graph Modeling (C-Graph) for cross-domain few-shot medical image segmentation.
- •Leverages structural consistency of medical images as a domain-transferable prior.
- •Introduces SPG layers, SMD mechanism, and CNC loss for improved performance.
- •Achieves state-of-the-art performance on multiple cross-domain benchmarks.
- •Preserves strong segmentation accuracy on the source domain.
Reference / Citation
View Original"The paper 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."