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

A. Kazi, , , H. Burwinkel, G. Vivar, B. Wiestler, , 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 (pre-print version is available online at arXiv) (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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