Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis

Abstract

In this paper, we propose a dual-graph learning convolutional network (dGLCN) to achieve interpretable Alzheimer’s disease (AD) diagnosis, by jointly investigating subject graph learning and feature graph learning in the graph convolution network (GCN) framework. Specifically, we first construct two initial graphs to consider both the subject diversity and the feature diversity. We further fuse these two initial graphs into the GCN framework so that they can be iteratively updated (i.e., dual-graph learning) while conducting representation learning. As a result, the dGLCN achieves interpretability in both subjects and brain regions through the subject importance and the feature importance, and the generalizability by overcoming the issues, such as limited subjects and noisy subjects. Experimental results on the Alzheimer’s disease neuroimaging initiative (ADNI) datasets show that our dGLCN outperforms all comparison methods for binary classification. The codes of dGLCN are available on https://github.com/xiaotingsong/dGLCN.