Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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A. Kazi, S. Shekarforoush, S. Krishna, H. Burwinkel, G. Vivar, B. Wiestler, K. Kortüm, A. Ahmadi, S. Albarqouni, N. Navab
Graph convolution based attention model for personalized disease prediction
22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, 2019 (bib)

Clinicians implicitly incorporate the complementarity of multi-modal data for disease diagnosis. Often a varied order of importance for this heterogeneous data is considered for personalized decisions. Current learning-based methods have achieved better performance with uniform attention to individual information but a very few have focused on patient-specific attention learning schemes for each modality. Towards this, we introduce a model which not only improves the disease prediction but also focuses on learning patient-specific order of importance for multi-modal data elements. In order to achieve this, we take advantage of LSTM-based attention mechanism and graph convolutional networks (GCNs) to design our model. GCNs learn multi-modal but class specific features from entire population of patients, where as the attention mechanism optimally fuses these multi-modal features into a final decision, separately for each patient. In this paper, we apply the proposed approach for disease prediction task for Parkinson’s and Alzheimer’s using two public medical datasets.
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