通过图匹配提升多中心脑影像诊断准确率
GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging
- 用图匹配建模不同中心脑结构间的关联关系
- 在三个公开数据集上均超越现有最佳方法
- 适合需要跨中心泛化的阿尔茨海默病研究者
阿尔茨海默病（AD）是一种影响数百万人的进行性神经退行性疾病，未来患病率将显著上升。早期诊断，尤其是在轻度认知障碍（MCI）阶段，对及时干预至关重要。结构性磁共振成像（sMRI）已成为检测与AD相关脑部变化的关键手段，但传统基于图的方法常受模态和跨中心异质性影响，限制了诊断性能。本文提出图匹配网络用于阿尔茨海默病诊断（GMN4AD），旨在建模来自多中心神经影像数据的异构脑图之间的交互关系。不同于传统方法独立处理每个脑图，GMN4AD利用图匹配捕捉跨图关联，提升诊断精度。此外，引入测试时领域自适应策略，结合对比学习缓解推理过程中的领域偏移。在三个公开AD数据集上的大量实验表明，GMN4AD在性能上优于现有最先进方法，为AD诊断提供了鲁棒且可泛化的解决方案。
原文摘要 · Abstract (English)
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of older adults, with prevalence expected to rise significantly in the coming years. Early diagnosis, particularly during the mild cognitive impairment (MCI) stage, is critical for timely intervention. Structural Magnetic Resonance Imaging (sMRI) has emerged as a key modality for detecting AD-related brain changes, but traditional graph-based approaches often struggle with modality and inter-site heterogeneity, limiting diagnostic performance. In this paper, we propose Graph Matching Network for Alzheimer's Disease Diagnosis (GMN4AD), designed to model interactions between heterogeneous brain graphs derived from neuroimaging data. Unlike conventional methods that treat each brain graph independently, GMN4AD leverages graph matching to capture cross-graph relationships, enhancing diagnostic precision. Furthermore, we introduce a test-time domain adaptation strategy that combines contrastive learning to mitigate domain shifts during inference. Extensive experiments on three public AD datasets demonstrate that GMN4AD achieves superior performance compared to state-of-the-art methods, offering a robust and generalizable solution for AD diagnosis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。