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Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease
In this work we propose three explainable deep learning architectures to automatically detect patients with Alzheimer's disease based on their language abilities. The architectures use: (1) only the part-of-speech features; (2) only language embedding features and (3) both of these feature classes via a unified architecture. We use self-attention mechanisms and interpretable 1-dimensional Convolutional Neural Network (CNN) to generate two types of explanations of the model's action: intra-classdoi:10.1101/2020.06.24.20139592 fatcat:uh7qq7fc4zcu7fblttt2bnjuny