Self-Attention Equipped Graph Convolutions for Disease Prediction

Autor:	Kazi, Anees, krishna, S. Arvind, Shekarforoush, Shayan, Kortuem, Karsten, Albarqouni, Shadi, Navab, Nassir
Rok vydání:	2018
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 4 pages, 4 figures, paper accepted in ISBI 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1812.09954 Zobrazit plný text záznamu View this record from Arxiv