GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging

2026-06-11

AI Summary

This work addresses the performance degradation in diagnosing Alzheimer’s disease—particularly at the mild cognitive impairment stage—in multi-site structural MRI due to modality- and site-induced heterogeneity. To mitigate domain shift, the authors propose a novel approach that integrates graph matching networks with test-time domain adaptation. The method leverages a graph matching mechanism to model cross-domain interactions among heterogeneous brain graphs and incorporates a contrastive learning–driven test-time adaptation strategy during inference. This is the first study to combine graph matching with test-time adaptation, demonstrating significantly improved diagnostic robustness and generalizability over existing methods across three public datasets, thereby enabling more reliable early-stage detection.

Abstract

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.

Problem

• Alzheimer's Disease
• Structural MRI
• Domain Heterogeneity
• Graph-based Diagnosis
• Multi-center Data

Innovation

• Graph Matching Network
• Test-Time Domain Adaptation
• Contrastive Learning
• Multi-centered sMRI
• Alzheimer's Disease Diagnosis