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An unsupervised XAI framework for dementia detection with context enrichment

Abstract Explainable Artificial Intelligence (XAI) methods enhance the diagnostic efficiency of clinical decision support systems by making the predictions of a convolutional neural network’s (CNN) on brain imaging more transparent and trustworthy. However, their clinical adoption is limited due to...

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Autori principali: Devesh Singh, Yusuf Brima, Fedor Levin, Martin Becker, Bjarne Hiller, Andreas Hermann, Irene Villar-Munoz, Lukas Beichert, Alexander Bernhardt, Katharina Buerger, Michaela Butryn, Peter Dechent, Emrah Düzel, Michael Ewers, Klaus Fliessbach, Silka D. Freiesleben, Wenzel Glanz, Stefan Hetzer, Daniel Janowitz, Doreen Görß, Ingo Kilimann, Okka Kimmich, Christoph Laske, Johannes Levin, Andrea Lohse, Falk Luesebrink, Matthias Munk, Robert Perneczky, Oliver Peters, Lukas Preis, Josef Priller, Johannes Prudlo, Diana Prychynenko, Boris S. Rauchmann, Ayda Rostamzadeh, Nina Roy-Kluth, Klaus Scheffler, Anja Schneider, Louise Droste zu Senden, Björn H. Schott, Annika Spottke, Matthis Synofzik, Jens Wiltfang, Frank Jessen, Marc-André Weber, Stefan J. Teipel, Martin Dyrba
Natura: Artigo
Lingua:Inglês
Pubblicazione: Nature Portfolio 2025-11-01
Serie:Scientific Reports
Soggetti:
Neurodegenerative diseases
Alzheimer’s disease
Frontotemporal dementia
Magnetic resonance imaging
Brain volumetry
Explainable artificial intelligence (XAI)
Accesso online:https://doi.org/10.1038/s41598-025-26227-2
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