  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 sensitivity analysis based
approach has difficulty handling loosely distributed cerebral cortex, and
approaches based on visualization of activations are constrained by the
resolution of the convolutional layer. The complementarity of these methods
improves the understanding of 3D-CNNs in Alzheimer's disease classification
from different perspectives.
