GapNet: characterizing the progression of alzheimer’s disease with deep learning

Yu-Wei G. Chang; Laura Natali; Oveis Jamialahmadi; Stefano Romeo; Joana B. Pereira; Giovanni Volpe

doi:10.1117/12.2594274

1 August 2021 GapNet: characterizing the progression of alzheimer’s disease with deep learning

Yu-Wei G. Chang, Laura Natali, Oveis Jamialahmadi, Stefano Romeo, Joana B. Pereira, Giovanni Volpe

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118040R (2021) https://doi.org/10.1117/12.2594274
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

A growing number of studies suggest that detection of Alzheimer’s disease can be improved by using information derived from distinct neuroimaging modalities. However, so far it remains unresolved how these modalities can be combined within a deep learning model approach. In this study, we proposed a deep-neural-network model GapNet that can work with incomplete dataset including baseline and longitudinal MR, amyloid-PET, and FDG-PET data. We verified the effectiveness of GapNet by comparing it to the conventional Vanilla neural networks and specifically testing their performance in discriminating between healthy controls and individuals with amyloid changes, which is an important early pathological marker in Alzheimer’s Disease. Results showed that, compared to the Vanilla networks, GapNet achieved higher classification accuracy. In sum, our finding suggested that the GapNet model is a promising deep learning approach for detecting Alzheimer’s disease with multi-modal neuroimaging

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Yu-Wei G. Chang, Laura Natali, Oveis Jamialahmadi, Stefano Romeo, Joana B. Pereira, and Giovanni Volpe "GapNet: characterizing the progression of alzheimer’s disease with deep learning", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118040R (1 August 2021); https://doi.org/10.1117/12.2594274

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KEYWORDS

Alzheimer's disease

Neural networks

Data modeling

Pathology

Cognitive modeling

Neuroimaging

Dementia

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