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, gender, and education level) of subjects. Specifically, we first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then extract multiple image patches around these detected landmarks. A deep multi-task multi-channel convolutional neural network is then developed for joint disease classification and clinical score regression. We train our model on a large multi-center cohort (i.e., ADNI-1) and test it on an independent cohort (i.e., ADNI-2). Experimental results demonstrate that DM 2 L is superior to the state-of-the-art approaches in brain diasease diagnosis.