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

Background: Although convolutional neural networks (CNN) 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 comprehensibi...

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Veröffentlicht in:	arXiv.org 2021-11
Hauptverfasser:	Dyrba, Martin, Hanzig, Moritz, Altenstein, Slawek, Bader, Sebastian, Ballarini, Tommaso, Brosseron, Frederic, Buerger, Katharina, Cantré, Daniel, Dechent, Peter, Dobisch, Laura, Düzel, Emrah, Ewers, Michael, Fliessbach, Klaus, Glanz, Wenzel, John-Dylan Haynes, Heneka, Michael T, Janowitz, Daniel, Keles, Deniz B, Kilimann, Ingo, Laske, Christoph, Maier, Franziska, Metzger, Coraline D, Munk, Matthias H, Perneczky, Robert, Peters, Oliver, Preis, Lukas, Priller, Josef, Rauchmann, Boris, Roy, Nina, Scheffler, Klaus, Schneider, Anja, Schott, Björn H, Spottke, Annika, Spruth, Eike J, Weber, Marc-André, Ertl-Wagner, Birgit, Wagner, Michael, Wiltfang, Jens, Jessen, Frank, Teipel, Stefan J
Format:	Artikel
Sprache:	eng
Schlagworte:	Abnormalities Accuracy Alzheimer's disease Alzheimers disease Artificial neural networks Atrophy Computer Science - Computer Vision and Pattern Recognition Dementia Diagnostic systems Hippocampus Independent sample Magnetic resonance imaging Model accuracy Neural networks Visualization
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