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

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 comprehensibility...

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Detalles Bibliográficos
Autores principales: 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, Haynes, John-Dylan, 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8611898/
https://www.ncbi.nlm.nih.gov/pubmed/34814936
http://dx.doi.org/10.1186/s13195-021-00924-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8611898/
https://www.ncbi.nlm.nih.gov/pubmed/34814936
http://dx.doi.org/10.1186/s13195-021-00924-2

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