Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer’s disease

Abstract Background Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehen...

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Main Authors: Martin Dyrba, Moritz Hanzig, Slawek Altenstein, Sebastian Bader, Tommaso Ballarini, Frederic Brosseron, Katharina Buerger, Daniel Cantré, Peter Dechent, Laura Dobisch, Emrah Düzel, Michael Ewers, Klaus Fliessbach, Wenzel Glanz, John-Dylan Haynes, Michael T. Heneka, Daniel Janowitz, Deniz B. Keles, Ingo Kilimann, Christoph Laske, Franziska Maier, Coraline D. Metzger, Matthias H. Munk, Robert Perneczky, Oliver Peters, Lukas Preis, Josef Priller, Boris Rauchmann, Nina Roy, Klaus Scheffler, Anja Schneider, Björn H. Schott, Annika Spottke, Eike J. Spruth, Marc-André Weber, Birgit Ertl-Wagner, Michael Wagner, Jens Wiltfang, Frank Jessen, Stefan J. Teipel, for the ADNI, AIBL, DELCODE study groups
Format: article
Language:EN
Published: BMC 2021
Subjects:
Alzheimer’s disease
Deep learning
Convolutional neural network
MRI
Layer-wise relevance propagation
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Neurology. Diseases of the nervous system
RC346-429
Online Access:https://doaj.org/article/4ea07e2a7a2f4319ab977e9fcdcd6e3d
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https://doaj.org/article/4ea07e2a7a2f4319ab977e9fcdcd6e3d

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