TY - GEN
T1 - Deep multi-task multi-channel learning for joint classification and regression of brain status
AU - Liu, Mingxia
AU - Zhang, Jun
AU - Adeli, Ehsan
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029506667&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66179-7_1
DO - 10.1007/978-3-319-66179-7_1
M3 - Conference contribution
AN - SCOPUS:85029506667
SN - 9783319661780
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 11
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
A2 - Maier-Hein, Lena
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Duchesne, Simon
A2 - Descoteaux, Maxime
A2 - Collins, D. Louis
PB - Springer Verlag
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Y2 - 11 September 2017 through 13 September 2017
ER -
