Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification

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...

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Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Tinauer, Christian, Damulina, Anna, Sackl, Maximilian, Soellradl, Martin, Achtibat, Reduan, Dreyer, Maximilian, Pahde, Frederik, Lapuschkin, Sebastian, Schmidt, Reinhold, Ropele, Stefan, Samek, Wojciech, Langkammer, Christian
Format: Article
Language:English
Subjects:
Alzheimer's disease
Artificial neural networks
Brain
Classification
Ganglia
Machine learning
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Summary: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.
ISSN:2331-8422