In this paper, a dynamic dual-graph fusion convolutional network is proposed
to improve Alzheimer's disease (AD) diagnosis performance. The following are
the 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 proposed
architecture can dynamically adjust the graph structure for GCN to produce
better diagnosis outcomes by learning the optimal underlying latent graph; (c)
incorporate feature graph learning and dynamic graph learning, giving those
useful features of subjects more weight while decreasing the weights of other
noise features. Experiments indicate that our model provides flexibility and
stability while achieving excellent classification results in AD diagnosis.

提出了一种动态双图融合卷积网络来提高阿尔茨海默病的诊断性能，该网络可以动态调整图结构以产生更好的诊断结果，并结合特征图和动态图学习来增加有用特征的权重并减少噪音特征的权重。实验证明该模型在阿尔茨海默病的诊断中具有灵活性和稳定性，并取得了优秀的分类结果。