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Neurological disorders generally involve multiple kinds of changes in the functional and structural properties of the brain. In this study, we develop a CNN-based multimodal deep learning pipeline by exploiting both functional and structural neuroimaging features to generate full-brain maps that encode significant differences between patient groups and between modalities in terms of their distinctive contribution towards diagnostic classification of Alzheimer’s disease. Through a repeated cross-validation procedure and robust statistical analysis, we show that our approach can be used to encode highly discriminative and abstract information from full-brain data, while also retaining the ability to identify and categorize significantly contributing voxel-level features based on their salient strength in various diagnostic and modality-related contexts. Our results on an Alzheimer’s disease classification task show that such approaches can be used for creating more elaborately defined biomarkers for brain disorders.
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Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer’s Disease Classification
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 convo- lutional layer. The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer’s disease classification from different perspectives.
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- Award ID(s):
- 1743050
- PAR ID:
- 10088685
- Date Published:
- Journal Name:
- AMIA ... Annual Symposium proceedings
- ISSN:
- 1559-4076
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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