Hippocampal representations for deep learning on Alzheimer’s disease

Sarasua, Ignacio; Pölsterl, Sebastian und Wachinger, Christian (21. Mai 2022): Hippocampal representations for deep learning on Alzheimer’s disease. In: Scientific Reports, Bd. 12

Abstract

Deep learning offers a powerful approach for analyzing hippocampal changes in Alzheimer’s disease (AD) without relying on handcrafted features. Nevertheless, an input format needs to be selected to pass the image information to the neural network, which has wide ramifications for the analysis, but has not been evaluated yet. We compare five hippocampal representations (and their respective tailored network architectures) that span from raw images to geometric representations like meshes and point clouds. We performed a thorough evaluation for the prediction of AD diagnosis and time-to-dementia prediction with experiments on an independent test dataset. In addition, we evaluated the ease of interpretability for each representation–network pair. Our results show that choosing an appropriate representation of the hippocampus for predicting Alzheimer’s disease with deep learning is crucial, since it impacts performance and ease of interpretation.

• Dokumententyp: Zeitschriftenartikel
• Publikationsform: Publisher's Version
• Fakultät: Medizin > Klinikum der LMU München > Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 2045-2322
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892