Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Artificial Functional MRI Data


Alzheimer’s disease (AD) is a neurodegenerative disorder where functional decits precede structural deformations. Various studies have demonstrated the efficacy of deep learning in diagnosing AD using imaging data, and that functional modalities are more helpful than structural counterparts over comparable sample size. To deal with the lack of large-scale functional data in the real world, we used a structure-to-function translation network to articially generate a previously non-existent spatiallymatched functional neuroimaging dataset from existing large-scale structural data. The articial functional data, generated with little cost, complemented the authentic structural data to further improve the performance of AD classication.

In International Society for Magnetic Resonance in Medicine (ISMRM) 1-Page Abstract
Chen Liu
Chen Liu
Computer Science PhD Student

Researcher in machine learning + healthcare.