Explaining 3D Cnns For Alzheimer'S Disease Classification On Smri Images With Multiple Rois

Classification of Alzheimerƒ??s disease from 3D structural Magnetic Resonance Imaging (sMRI) with deep neural networks has shown promising results in recent years. The decision interpretation of these networks is essential to aid medical experts to understand and rely on the results provided by such models. In this paper, we propose an adaptation of a recently developed feature-based explanation method and apply it to a 3D CNN architecture for the binary classification of Alzheimerƒ??s disease and Normal Control from the hippocampal ROIs of brain sMRIs. We also compare our method to the state-of-the-art LRP method.