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Document type:
Konferenzbeitrag 
Author(s):
Burwinkel, H.; Kazi, A.; Vivar, G.; Albarqouni, S.; Zahnd, G.; Navab, N.; Ahmadi, A. 
Title:
Adaptive image-feature learning for disease classification using inductive graph networks 
Abstract:
Recently, Geometric Deep Learning (GDL) has been introduced as a novel and versatile framework for computer-aided disease classification. GDL uses patient meta-information such as age and gender to model patient cohort relations in a graph structure. Concepts from graph signal processing are leveraged to learn the optimal mapping of multi-modal features, e.g. from images to disease classes. Related studies so far have considered image features that are extracted in a pre-processing step. We hypo...    »
 
Keywords:
MICCAI 
Book / Congress title:
International Conference on Medical Image Computing and Computer-Assisted Intervention 
Organization:
Springer 
Year:
2019 
Pages:
640--648