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.

翻译：阿尔茨海默病是一种影响数百万老年人的进行性神经退行性疾病，其患病率预计在未来数年内将显著上升。早期诊断（尤其是轻度认知障碍阶段）对及时干预至关重要。结构磁共振成像已成为检测AD相关脑部变化的关键影像学手段，但传统的图分析方法往往难以应对成像模态异质性和跨站点差异，导致诊断性能受限。本文提出面向阿尔茨海默病诊断的图匹配网络（GMN4AD），旨在建模神经影像数据衍生的异质性脑图之间的交互关系。不同于传统方法独立处理每个脑图的方式，GMN4AD利用图匹配技术捕捉跨图关联性，从而提升诊断精度。此外，我们引入一种测试时域适应策略，结合对比学习来缓解推理过程中的域偏移问题。在三个公开AD数据集上的大量实验表明，与现有最先进方法相比，GMN4AD实现了更优的诊断性能，为AD诊断提供了鲁棒且可泛化的解决方案。