Geometric deep learning provides a principled and versatile manner for the
integration of imaging and non-imaging modalities in the medical domain. Graph
Convolutional Networks (GCNs) in particular have been explored on a wide
variety of problems such as disease prediction, segmentation, and matrix
completion by leveraging large, multimodal datasets. In this paper, we
introduce a new spectral domain architecture for deep learning on graphs for
disease prediction. The novelty lies in defining geometric 'inception modules'
which are capable of capturing intra- and inter-graph structural heterogeneity
during convolutions. We design filters with different kernel sizes to build our
architecture. We show our disease prediction results on two publicly available
datasets. Further, we provide insights on the behaviour of regular GCNs and our
proposed model under varying input scenarios on simulated data.

本文介绍了一种新的图形领域体系结构，用于基于图形的深度学习用于疾病预测，该体系结构利用不同内核大小的滤波器来构建，特点是定义了几何 “内核模块”，能够在卷积期间捕获结构异质性。研究展示了该模型在两个公开数据集上的疾病预测结果，并提供了在模拟数据的不同输入情况下，常规 GCNs 和我们提出的模型的行为洞见。