Multi-modal disease classification in incomplete datasets using geometric matrix completion
Vivar G, Zwergal A, Navab N, Ahmadi SA (2018)
Publication Type: Conference contribution
Publication year: 2018
Journal
Publisher: Springer Verlag
Book Volume: 11044 LNCS
Pages Range: 24-31
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030006884
DOI: 10.1007/978-3-030-00689-1_3
Abstract
In large population-based studies and in clinical routine, tasks like disease diagnosis and progression prediction are inherently based on a rich set of multi-modal data, including imaging and other sensor data, clinical scores, phenotypes, labels and demographics. However, missing features, rater bias and inaccurate measurements are typical ailments of real-life medical datasets. Recently, it has been shown that deep learning with graph convolution neural networks (GCN) can outperform traditional machine learning in disease classification, but missing features remain an open problem. In this work, we follow up on the idea of modeling multi-modal disease classification as a matrix completion problem, with simultaneous classification and non-linear imputation of features. Compared to methods before, we arrange subjects in a graph-structure and solve classification through geometric matrix completion, which simulates a heat diffusion process that is learned and solved with a recurrent neural network. We demonstrate the potential of this method on the ADNI-based TADPOLE dataset and on the task of predicting the transition from MCI to Alzheimer’s disease. With an AUC of 0.950 and classification accuracy of 87%, our approach outperforms standard linear and non-linear classifiers, as well as several state-of-the-art results in related literature, including a recently proposed GCN-based approach.
Involved external institutions
Germany (DE)How to cite
APA:
Vivar, G., Zwergal, A., Navab, N., & Ahmadi, S.A. (2018). Multi-modal disease classification in incomplete datasets using geometric matrix completion. In Danail Stoyanov, Aristeidis Sotiras, Bartlomiej Papiez, Adrian V. Dalca, Anne Martel, Sarah Parisot, Enzo Ferrante, Lena Maier-Hein, Mert R. Sabuncu, Li Shen, Zeike Taylor (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 24-31). Granada, ESP: Springer Verlag.
MLA:
Vivar, Gerome, et al. "Multi-modal disease classification in incomplete datasets using geometric matrix completion." Proceedings of the 2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and 1st International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018 Held in Conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Danail Stoyanov, Aristeidis Sotiras, Bartlomiej Papiez, Adrian V. Dalca, Anne Martel, Sarah Parisot, Enzo Ferrante, Lena Maier-Hein, Mert R. Sabuncu, Li Shen, Zeike Taylor, Springer Verlag, 2018. 24-31.
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