Deep Multi-Task Multi-Channel Learning for Joint Classification and Regression of Brain Status.

ABSTRACT: Jointly identifying brain diseases and predicting clinical scores have attracted increasing attention in the domain of computer-aided diagnosis using magnetic resonance imaging (MRI) data, since these two tasks are highly correlated. Although several joint learning models have been developed, most existing methods focus on using human-engineered features extracted from MRI data. Due to the possible heterogeneous property between human-engineered features and subsequent classification/regression models, those methods may lead to sub-optimal learning performance. In this paper, we propose a deep multi-task multi-channel learning (DM²L) framework for simultaneous classification and regression for brain disease diagnosis, using MRI data and personal information (i.e., age, ge

SUBMITTER: Liu M 

PROVIDER: S-EPMC5942232 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature