Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis.

ABSTRACT: In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic resonance imaging (MRI) have attracted increasing attention since these two tasks are highly correlated. Most of existing joint learning approaches require hand-crafted feature representations for MR images. Since hand-crafted features of MRI and classification/regression models may not coordinate well with each other, conventional methods may lead to sub-optimal learning performance. Also, demographic information (e.g., age, gender, and education) of subjects may also be related to brain status, and thus can help improve the diagnostic performance. However, conventional joint learning methods seldom incorporate such demographic information int

SUBMITTER: Liu M 

PROVIDER: S-EPMC6764421 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature