  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.
