Alzheimer's Disease (AD) is a progressive neurodegenerative disorder affecting 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 the 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.
Blogger's Review: The innovation of GMN4AD lies in its ability to leverage graph matching to capture relationships between brain graphs and employ contrastive learning for domain adaptation, addressing the shortcomings of traditional methods in handling heterogeneity. Its superior performance offers new hope for early diagnosis of Alzheimer's disease, making it worthy of clinical application.