Self-Attention Equipped Graph Convolutions for Disease Prediction

SELF-A TTENTION EQUIPPED GRAPH CONVOLUTIONS FOR DISEASE PREDICTION Anees Kazi 1, S.Arvind krishna 2, Shayan Shekarforoush 3, Karsten Kortuem 4, Shadi Albarqouni 1, Nassir Navab 1, 5 1 Computer Aided Medical Procedures, Technische Universität München, Germany 2 National Institute of Technology Tiruchirappalli, India 3 Sharif University of Technology, Iran 4 Augenklinik der Universität, Klinikum der Universität München, Germany 5 Johns Hopkins University, Baltimore MD, USA 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.