Latent-Graph Learning for Disease Prediction
Cosmo L, Kazi A, Ahmadi SA, Navab N, Bronstein M (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12262 LNCS
Pages Range: 643-653
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lima, PER
ISBN: 9783030597122
DOI: 10.1007/978-3-030-59713-9_62
Abstract
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are very sensitive to the graph structure. In this paper, we demonstrate for the first time in the CADx domain that it is possible to learn a single, optimal graph towards the GCN’s downstream task of disease classification. To this end, we propose a novel, end-to-end trainable graph learning architecture for dynamic and localized graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is spatial and inductive, and can thus infer previously unseen patients as well. We demonstrate significant classification improvements with our learned graph on two CADx problems in medicine. We further explain and visualize this result using an artificial dataset, underlining the importance of graph learning for more accurate and robust inference with GCNs in medical applications.
Involved external institutions
Università della Svizzera italiana (USI)
Switzerland (CH)
Technische Universität München (TUM)
Germany (DE)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
Switzerland (CH)
Technische Universität München (TUM)
Germany (DE)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
How to cite
APA:
Cosmo, L., Kazi, A., Ahmadi, S.A., Navab, N., & Bronstein, M. (2020). Latent-Graph Learning for Disease Prediction. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 643-653). Lima, PER: Springer Science and Business Media Deutschland GmbH.
MLA:
Cosmo, Luca, et al. "Latent-Graph Learning for Disease Prediction." Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, PER Ed. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz, Springer Science and Business Media Deutschland GmbH, 2020. 643-653.
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