• DOI:10.1007/978-3-030-32226-7_71
Corpus ID: 147704198

Adaptive image-feature learning for disease classification using inductive graph networks

@article{Burwinkel2019AdaptiveIL,
  title={Adaptive image-feature learning for disease classification using inductive graph networks},
  author={Hendrik Burwinkel and Anees Kazi and G. Vivar and Shadi Albarqouni and G. Zahnd and Nassir Navab and Seyed-Ahmad Ahmadi},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.03036}
}

Hendrik Burwinkel, Anees Kazi, +4 authors Seyed-Ahmad Ahmadi

Published 2019

Computer Science, Engineering, Mathematics

ArXiv

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… CONTINUE READING

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