Deep Multi-task Multi-channel Learning for Joint Classification and Regression of Brain Status

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


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 (DM\(^2\)L) 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.

Supplementary material

455908_1_En_1_MOESM1_ESM.pdf (220 kb)
Supplementary material 1 (pdf 219 KB)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

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