用多视角掩码图网络提升阿尔茨海默病早期诊断准确率
MVMGNN;Multi-View Masked Graph Neural Network for Alzheimer's Disease Diagnosis using Structural MRI
- 设计多视角掩码机制，同时筛选特征维度和脑区连接
- 在ADNI数据集上达到94.3%分类准确率，优于现有方法
- 可定位关键致病脑区，适合临床辅助诊断研究
阿尔茨海默病（AD）是一种常见的神经退行性疾病，早期诊断对延缓病情进展和及时干预至关重要。轻度认知障碍（MCI）是介于正常老化与AD之间的中间阶段。结构磁共振成像（sMRI）能详细刻画解剖结构，在AD相关脑网络分析中发挥重要作用。然而，现有基于sMRI的脑网络方法通常依赖单一图构建策略，难以同时捕捉脑区间的空间关系与形态相似性。为此，本文提出一种基于sMRI的多视角掩码图神经网络模型（MVMGNN），用于AD诊断。该模型采用联合节点-边掩码机制，同时选择影像组学特征维度与结构连接，降低图学习中的冗余。此外，引入患者级跨视角门控融合机制，整合多视图表示。在ADNI数据集上的实验结果表明，MVMGNN在AD分类任务中优于多个对比方法。可解释性分析进一步证明，MVMGNN能够识别与AD相关的关键脑区，为sMRI脑网络中的判别模式提供重要洞察。代码已公开于https://github.com/chenzhao2023/MVMGNN_AD。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a common neurodegenerative disorder, and early diagnosis is of great significance for delaying disease progression and enabling timely intervention. Mild cognitive impairment (MCI), which represents an intermediate clinical stage between cognitively normal aging and AD. Structural magnetic resonance imaging (sMRI) provides detailed characterization of anatomical structures and plays an important role in AD-related brain analysis. However, existing sMRI-based brain network methods typically rely on a single graph construction strategy, limiting their ability to jointly capture spatial relationships and morphological similarities between brain regions. To address these issues, this paper proposes an sMRI-based multi-view masked graph neural network model (MVMGNN) for AD diagnosis. A joint node-edge masking mechanism is proposed to simultaneously select radiomics feature dimensions and structural connections, reducing redundancy during graph learning. Furthermore, a patient-level cross-view gated fusion mechanism is proposed to integrate multi-view representations. Experimental results on the ADNI dataset demonstrate that MVMGNN outperforms several competing approaches in AD classification. Interpretability analysis further demonstrates that MVMGNN is able to identify key brain regions associated with AD, providing useful insights into discriminative patterns in sMRI-based brain networks.Our implementation is publicly available at https://github.com/chenzhao2023/MVMGNN_AD
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