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We propose a mesh-based technique to aid in the classification of Alzheimer'sdisease dementia (ADD) using mesh representations of the cortex and subcorticalstructures. Deep learning methods for classification tasks that utilizestructural neuroimaging often require extensive learning parameters tooptimize. Frequently, these approaches for automated medical diagnosis alsolack visual interpretability for areas in the brain involved in making adiagnosis. This work: (a) analyzes brain shape using surface information of thecortex and subcortical structures, (b) proposes a residual learning frameworkfor state-of-the-art graph convolutional networks which offer a significantreduction in learnable parameters, and (c) offers visual interpretability ofthe network via class-specific gradient information that localizes importantregions of interest in our inputs. With our proposed method leveraging the useof cortical and subcortical surface information, we outperform other machinelearning methods with a 96.35% testing accuracy for the ADD vs. healthy controlproblem. We confirm the validity of our model by observing its performance in a25-trial Monte Carlo cross-validation. The generated visualization maps in ourstudy show correspondences with current knowledge regarding the structurallocalization of pathological changes in the brain associated to dementia of theAlzheimer's type.

lecture notes in computer science

Interpretation of Brain Morphology in Association to Alzheimer's Disease  Dementia Classification Using Graph Convolutional Networks on Triangulated  Meshes