InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Kazi A, Shekarforoush S, Krishna SA, Burwinkel H, Vivar G, Kortum K, Ahmadi SA, Albarqouni S, Navab N (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11492 LNCS
Pages Range: 73-85
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Hong Kong, CHN
ISBN: 9783030203504
DOI: 10.1007/978-3-030-20351-1_6
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.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Sharif University of Technology / دانشگاه صنعتی شریف (SUT)
Iran, Islamic Republic of (IR)
National Institute of Technology, Tiruchirappalli (NIT Trichy)
India (IN)
Klinikum der Universität München (Großhadern und Innenstadt)
Germany (DE)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
Germany (DE)
Sharif University of Technology / دانشگاه صنعتی شریف (SUT)
Iran, Islamic Republic of (IR)
National Institute of Technology, Tiruchirappalli (NIT Trichy)
India (IN)
Klinikum der Universität München (Großhadern und Innenstadt)
Germany (DE)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
How to cite
APA:
Kazi, A., Shekarforoush, S., Krishna, S.A., Burwinkel, H., Vivar, G., Kortum, K.,... Navab, N. (2019). InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction. In Siqi Bao, James C. Gee, Paul A. Yushkevich, Albert C.S. Chung (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 73-85). Hong Kong, CHN: Springer Verlag.
MLA:
Kazi, Anees, et al. "InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction." Proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, Hong Kong, CHN Ed. Siqi Bao, James C. Gee, Paul A. Yushkevich, Albert C.S. Chung, Springer Verlag, 2019. 73-85.
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