用双图注意力网络融合多模态数据，提升帕金森和阿尔茨海默病早期诊断准确率
DW-DGAT: Dynamically Weighted Dual Graph Attention Network for Neurodegenerative Disease Diagnosis
- 构建脑区与样本间双图结构，同时捕捉局部和全局特征
- 在PPMI和ADNI数据集上达到当前最优诊断性能
- 动态加权机制有效缓解疾病样本不平衡问题
帕金森病（PD）和阿尔茨海默病（AD）是全球最常见的两种不可治愈神经退行性疾病，早期诊断对延缓病情进展至关重要。然而，多指标数据的高维性、结构多样性，以及神经影像与表型数据的异质性、类别不平衡等问题，给早期诊断带来巨大挑战。为此，我们提出动态加权双图注意力网络（DW-DGAT），整合三项创新：(1) 通用数据融合策略，用于合并三种结构形式的多指标数据；(2) 基于脑区与样本间关系的双图注意力架构，以提取微观与宏观特征；(3) 结合两类稳定有效的损失函数的类别权重生成机制，缓解类别不平衡问题。基于帕金森进展标志物计划（PPMI）和阿尔茨海默病神经影像计划（ADNI）的严格实验表明，该方法在早期诊断任务中表现达到当前最优水平。
原文摘要 · Abstract (English)
Parkinson's disease (PD) and Alzheimer's disease (AD) are the two most prevalent and incurable neurodegenerative diseases (NDs) worldwide, for which early diagnosis is critical to delay their progression. However, the high dimensionality of multi-metric data with diverse structural forms, the heterogeneity of neuroimaging and phenotypic data, and class imbalance collectively pose significant challenges to early ND diagnosis. To address these challenges, we propose a dynamically weighted dual graph attention network (DW-DGAT) that integrates: (1) a general-purpose data fusion strategy to merge three structural forms of multi-metric data; (2) a dual graph attention architecture based on brain regions and inter-sample relationships to extract both micro- and macro-level features; and (3) a class weight generation mechanism combined with two stable and effective loss functions to mitigate class imbalance. Rigorous experiments, based on the Parkinson Progression Marker Initiative (PPMI) and Alzheimer's Disease Neuroimaging Initiative (ADNI) studies, demonstrate the state-of-the-art performance of our approach.
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