Mental Disorders Prediction with Heterogeneous Graph Convolutional Network

Abstract

In the medical imaging field, Computer-Aided Detection (CADe) has greatly benefited from the recent development of Graph Convolutional Networks (GCNs). GCN-based predictive models require building a population graph to detect the disease states of each subject, based on imaging and non-imaging data. Until now, all existing population-level methods are homogeneous, failing to consider sex differences. To address this issue, we present a heterogeneous population graph convolutional network with hierarchical attention mechanisms, including intra-level and inter-level attention. Specifically, the intra-level attention layer is aimed at learning differences and similarities between the sexes, while the inter-level attention layer is responsible for information integration by assigning weights to different features. The objective is to obtain node embeddings describing individual characteristics completely and provide discriminative inputs to classifiers. Compared to benchmark models, our proposal achieves satisfying prediction results on three datasets, illustrating the framework's ability to extract predictive attributes from medical multimodal data.