Self-attention equipped graph convolutions for disease prediction

Kazi A, Krishna SA, Shekarforoush S, Kortuem K, Albarqouni S, Navab N (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Proceedings - International Symposium on Biomedical Imaging

Publisher: IEEE Computer Society

Book Volume: 2019-April

Pages Range: 1896-1899

Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging

Event location: Venice, ITA

ISBN: 9781538636411

DOI: 10.1109/ISBI.2019.8759274

Abstract

Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test, demographics, etc.) data can be collected together and used for disease prediction. Such diverse data gives complementary information about the patient's condition to make an informed diagnosis. A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction. We propose a graph convolution based deep model which takes into account the distinctiveness of each element of the multi-modal data. We incorporate a novel self-attention layer, which weights every element of the demographic data by exploring its relation to the underlying disease. We demonstrate the superiority of our developed technique in terms of computational speed and performance when compared to state-of-the-art methods. Our method outperforms other methods with a significant margin.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) National Institute of Technology, Tiruchirappalli (NIT Trichy) IN India (IN) Sharif University of Technology / دانشگاه صنعتی شریف (SUT) IR Iran, Islamic Republic of (IR) Klinikum der Universität München DE Germany (DE)

How to cite

APA:

Kazi, A., Krishna, S.A., Shekarforoush, S., Kortuem, K., Albarqouni, S., & Navab, N. (2019). Self-attention equipped graph convolutions for disease prediction. In Proceedings - International Symposium on Biomedical Imaging (pp. 1896-1899). Venice, ITA: IEEE Computer Society.

MLA:

Kazi, Anees, et al. "Self-attention equipped graph convolutions for disease prediction." Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, ITA IEEE Computer Society, 2019. 1896-1899.

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