Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks
Burwinkel H, Kazi A, Vivar G, Albarqouni S, Zahnd G, Navab N, Ahmadi SA (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 11769 LNCS
Pages Range: 640-648
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030322250
DOI: 10.1007/978-3-030-32226-7_71
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 hypothesize that such an approach prevents the network from optimizing feature representations towards achieving the best performance in the graph network. We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly. We validate this architecture against state-of-the-art inductive graph networks and demonstrate significantly improved classification scores on a modified MNIST toy dataset, as well as comparable classification results with higher stability on a chest X-ray image dataset. Additionally, we explain how the structural information of the graph affects both the image filters and the feature learning.
Involved external institutions
Germany (DE)How to cite
APA:
Burwinkel, H., Kazi, A., Vivar, G., Albarqouni, S., Zahnd, G., Navab, N., & Ahmadi, S.-A. (2019). Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 640-648). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.
MLA:
Burwinkel, Hendrik, et al. "Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 640-648.
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