Early Diagnosis of Alzheimer's Disease Based on Multimodal Hypergraph Attention Network

Alzheimer's disease (AD) is a typical neurodegenerative disease involving multiple pathogenic factors. Early detection is the key to effective treatment of AD. However, most methods are developed based on data from a single modality, and ignore the relationships among subjects. In machine learning problems, hypergraph can be used to express the relationships between objects. In light of this, a framework for early diagnosis of Alzheimer's disease based on multimodal hypergraph attention network is proposed in this paper. Specifically, we combine multimodal features to construct cross modal hypergraph, which represents the high-order structural relationships among subjects. Finally, a hypergraph attention network is used to fuse hypergraphs and perform the final classification. Our experimental results on the Alzheimer Disease Neuroimaging Initiative (ADNI) database show that our proposed method has better classification performance than the most advanced methods.