Explainable concept mappings of MRI: Revealing the mechanisms underlying
  deep learning-based brain disease classification
Christian Tinauer Anna Damulina Maximilian Sackl Martin Soellradl Reduan Achtibat Maximilian Dreyer Frederik Pahde Sebastian Lapuschkin Reinhold Schmidt Stefan Ropele Wojciech Samek Christian Langkammer
Motivation. While recent studies show high accuracy in the classification of
Alzheimer's disease using deep neural networks, the underlying learned concepts
have not been investigated.
  Goals. To systematically identify changes in brain regions through concepts
learned by the deep neural network for model validation.
  Approach. Using quantitative R2* maps we separated Alzheimer's patients
(n=117) from normal controls (n=219) by using a convolutional neural network
and systematically investigated the learned concepts using Concept Relevance
Propagation and compared these results to a conventional region of
interest-based analysis.
  Results. In line with established histological findings and the region of
interest-based analyses, highly relevant concepts were primarily found in and
adjacent to the basal ganglia.
  Impact. The identification of concepts learned by deep neural networks for
disease classification enables validation of the models and could potentially
improve reliability.