SELF-ATTENTION EQUIPPED GRAPH CONVOLUTIONS FOR DISEASE PREDICTION

Kazi, Anees; Krishna, S. Arvind; Shekarforoush, Shayan; Kortuem, Karsten; Albarqouni, Shadi und Navab, Nassir (2019): SELF-ATTENTION EQUIPPED GRAPH CONVOLUTIONS FOR DISEASE PREDICTION. In: 2019 Ieee 16Th International Symposium on Biomedical Imaging (Isbi 2019): S. 1896-1899

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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1945-7928
• Sprache: Englisch