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Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification
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
pmid:30815203
pmcid:PMC6371279
fatcat:3tyqplujlnfqpdahs5ofk4irhq