CNN and Riemannian geometry for Alzheimer's disease progression classification

Antoine de Mori; Clovis Tauber

doi:10.1117/12.3006667

3 April 2024 CNN and Riemannian geometry for Alzheimer's disease progression classification

Antoine de Mori, Clovis Tauber

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 1292713 (2024) https://doi.org/10.1117/12.3006667
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Alzheimer’s Disease (AD) is one of the most important neurodegenerative diseases, both difficult to diagnose and not curable. Based on multimodal data from the Alzheimer’s Disease Neuroimaging Initiative, we implemented a longitudinal continuous model able to characterize patient trajectories and we use it to predict AD development for the next 3 years based on 6 observations. In our experiments, the proposed approach reached a 0.881 prediction accuracy, illustrating the potential of the proposed approach for diagnosis and prognosis.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Antoine de Mori and Clovis Tauber "CNN and Riemannian geometry for Alzheimer's disease progression classification", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 1292713 (3 April 2024); https://doi.org/10.1117/12.3006667

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