InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

Anees Kazi, Shayan Shekarforoush, S. Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kortüm, Seyed Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

102 Scopus citations

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.

Original languageEnglish
Title of host publicationInformation Processing in Medical Imaging - 26th International Conference, IPMI 2019, Proceedings
EditorsSiqi Bao, James C. Gee, Paul A. Yushkevich, Albert C.S. Chung
PublisherSpringer Verlag
Pages73-85
Number of pages13
ISBN (Print)9783030203504
DOIs
StatePublished - 2019
Event26th International Conference on Information Processing in Medical Imaging, IPMI 2019 - Hong Kong, China
Duration: 2 Jun 20197 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11492 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Information Processing in Medical Imaging, IPMI 2019
Country/TerritoryChina
CityHong Kong
Period2/06/197/06/19

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