Abstract

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Title:: Advances in multimodal deep learning for early detection of Alzheimers disease

Author(s):: LI DI1; 2; YAO Xufeng2; 1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China

Keywords:: Keywords: Alzheimers disease multimodal deep learning neuroimaging

Abstract:: Alzheimers disease (AD) is a chronic neurodegenerative disease that mainly affects neurons in the brain, especially in regions related to memory, thinking, and behavior. During the auxiliary diagnosis of AD, massive data from imaging, genetics, transcriptomics as well as clinical features provide a new basis for mining potential molecular markers and the early diagnosis and intervention of AD. In recent years, deep learning models have shown strong feature learning and prediction capabilities in AD image classification and the researchers will effectively integrate various modal data to provide richer complementary information for further improving the classification performance. Herein the review introduces the commonly used neuroimaging data sets and evaluation criteria for AD, analyzes the application of various modal data in AD classification, focusing on the application of multimodal data in AD classification diagnosis, discuss the application of the classic deep learning network model in AD classification diagnosis, aiming to provide ideas for further research on multimodal deep learning technology.