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Erschienen in:
2019 | OriginalPaper | Buchkapitel
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
verfasst von : Anees Kazi, Shayan Shekarforoush, S. Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kortüm, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab
Erschienen in: Information Processing in Medical Imaging
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Abstract
Geometric deep learning provides a principled and versatile manner for 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, multi-modal 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.