Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification

Chengliang Yang, Anand Rangarajan, Sanjay Ranka
2018 AMIA Annual Symposium Proceedings  
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis. Comparative analysis show that the
more » ... tivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained by the resolution of the convo-lutional layer. The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives.
pmid:30815203 pmcid:PMC6371279 fatcat:3tyqplujlnfqpdahs5ofk4irhq