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In this paper, a dynamic dual-graph fusion convolutional network is proposedto improve Alzheimer's disease (AD) diagnosis performance. The following arethe paper's main contributions: (a) propose a novel dynamic GCN architecture,which is an end-to-end pipeline for diagnosis of the AD task; (b) the proposedarchitecture can dynamically adjust the graph structure for GCN to producebetter diagnosis outcomes by learning the optimal underlying latent graph; (c)incorporate feature graph learning and dynamic graph learning, giving thoseuseful features of subjects more weight while decreasing the weights of othernoise features. Experiments indicate that our model provides flexibility andstability while achieving excellent classification results in AD diagnosis.

Dynamic Dual-Graph Fusion Convolutional Network For Alzheimer's Disease Diagnosis