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Document type:
Konferenzbeitrag 
Author(s):
Kazi, A.; Shekarforoush, S.; Krishna, S.; Burwinkel, H.; Vivar, G.; Kortü m, K.; Ahmadi, A.; Albarqouni, S.; Navab, N. 
Title:
InceptionGCN : Receptive Field Aware Graph Convolutional Network for Disease Prediction (Oral) 
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, 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 geometr...    »
 
Keywords:
IPMI,camp 
Book / Congress title:
International Conference on Information Processing in Medical Imaging 
Organization:
Springer 
Year:
2019 
Pages:
73--85