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

• URL: http://arxiv.org/abs/2512.21683v1
• Date: Thu, 25 Dec 2025 14:00:17 GMT
• Title: Contrastive Graph Modeling for Cross-Domain Few-Shot Medical Image Segmentation
• Authors: Yuntian Bo, Tao Zhou, Zechao Li, Haofeng Zhang, Ling Shao,
• Abstract summary: Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications.<n>We present Contrastive Graph Modeling (C-Graph), a framework that leverages the structural consistency of medical images as a reliable domain-transferable prior.
• Score: 58.41482540044918
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Towards Pre-trained Graph Condensation via Optimal Transport [52.6504753271008]
  Graph condensation aims to distill the original graph into a small-scale graph, mitigating redundancy and accelerating GNN training.<n> conventional GC approaches heavily rely on rigid GNNs and task-specific supervision.<n>Pre-trained Graph Condensation (PreGC) via optimal transport is proposed to transcend the limitations of task- and architecture-dependent GC methods.
  arXiv  Detail & Related papers  (2025-09-18T08:13:24Z)
• Can Diffusion Models Bridge the Domain Gap in Cardiac MR Imaging? [2.0202525145391093]
  We propose a diffusion model (DM) trained on a source domain that generates synthetic cardiac MR images that resemble a given reference.<n>The synthetic data maintains spatial and structural fidelity, ensuring similarity to the source domain and compatibility with the segmentation mask.<n>We explore domain generalisation, where, domain-invariant segmentation models are trained on synthetic source domain data, and domain adaptation, where, we shift target domain data towards the source domain using the DM.
  arXiv  Detail & Related papers  (2025-08-08T13:57:48Z)
• ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions [64.17845687013434]
  We propose ReDiSC, a structured diffusion model for structured node classification.<n>We show that ReDiSC achieves superior or highly competitive performance compared to state-of-the-art GNN, label propagation, and diffusion-based baselines.<n> Notably, ReDiSC scales effectively to large-scale datasets on which previous structured diffusion methods fail due to computational constraints.
  arXiv  Detail & Related papers  (2025-07-19T04:46:53Z)
• Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks [50.42343781348247]
  We develop a graph Poisson factor analysis (GPFA) which provides analytic conditional posteriors to improve the inference accuracy. We also extend GPFA to a multi-stochastic-layer version named graph Poisson gamma belief network (GPGBN) to capture the hierarchical document relationships at multiple semantic levels. Our models can extract high-quality hierarchical latent document representations and achieve promising performance on various graph analytic tasks.
  arXiv  Detail & Related papers  (2024-10-13T02:22:14Z)
• UnSeGArmaNet: Unsupervised Image Segmentation using Graph Neural Networks with Convolutional ARMA Filters [10.940349832919699]
  We propose an unsupervised segmentation framework with a pre-trained ViT. By harnessing the graph structure inherent within the image, the proposed method achieves a notable performance in segmentation. The proposed method provides state-of-the-art performance (even comparable to supervised methods) on benchmark image segmentation datasets.
  arXiv  Detail & Related papers  (2024-10-08T15:10:09Z)
• Towards Unified and Adaptive Cross-Domain Collaborative Filtering via Graph Signal Processing [48.04521184170971]
  Cross-Domain Recommendation (CDR) has emerged as a promising solution by leveraging dense domains to improve recommendations in sparse target domains.<n>We propose CGSP, a unified and adaptive CDR framework based on graph signal processing (GSP)
  arXiv  Detail & Related papers  (2024-07-17T07:52:45Z)
• Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups [10.699937593876669]
  Cross-network node classification extends GNN techniques to account for domain drift. We present OTGCN, a powerful, novel approach to cross-network node classification. We demonstrate the effectiveness of this approach at classifying Autism Spectrum Disorder subjects.
  arXiv  Detail & Related papers  (2024-01-10T18:04:12Z)
• Robust and Efficient Segmentation of Cross-domain Medical Images [37.38861543166964]
  We propose a generalizable knowledge distillation method for robust and efficient segmentation of medical images. We propose two generalizable knowledge distillation schemes, Dual Contrastive Graph Distillation (DCGD) and Domain-Invariant Cross Distillation (DICD) In DICD, the domain-invariant semantic vectors from the two models (i.e., teacher and student) are leveraged to cross-reconstruct features by the header exchange of MSAN.
  arXiv  Detail & Related papers  (2022-07-26T15:55:36Z)
• Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation [12.863227646939563]
  We propose Contrastive Disentangle Domain (CDD) network for generalizable medical image segmentation. We first introduce a disentangle network to decompose medical images into an anatomical representation factor and a modality representation factor. We then propose a domain augmentation strategy that can randomly generate new domains for model generalization training.
  arXiv  Detail & Related papers  (2022-05-13T10:32:41Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.