AI 中文总结
针对阿尔茨海默病诊断问题，提出基于结构磁共振成像的多视图掩码图神经网络模型MVMGNN，通过联合节点-边掩码和患者级跨视图门控融合机制，在ADNI数据集实验中表现优异，能识别关键脑区。
AI 中文摘要
阿尔茨海默病（AD）是常见神经退行性疾病，早期诊断对延缓病情发展和及时干预意义重大。轻度认知障碍（MCI）是认知正常衰老与AD之间的中间临床阶段。结构磁共振成像（sMRI）在AD相关脑分析中作用重要。但现有基于sMRI的脑网络方法依赖单一图构建策略。本文提出基于sMRI的多视图掩码图神经网络模型（MVMGNN）用于AD诊断。提出联合节点-边掩码机制减少图学习冗余，还提出患者级跨视图门控融合机制整合多视图表示。在ADNI数据集上实验结果表明MVMGNN在AD分类中优于几种竞争方法，可识别与AD相关关键脑区。
英文摘要
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
