Keywords: Alzheimer's Disease, Relaxometry, xAI, Explainable, Deep Learning
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.
Goal(s): To systematically identify the concepts learned by the deep neural network for model validation.
Approach: Using R2* maps we separated Alzheimer's patients (n=117) from healthy controls (n=219) by using a deep neural network and systematically investigated the learned concepts using Concept Relevance Propagation (CRP).
Results: In line with established histological findings, 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 improves reliability.
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