multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data
发表时间：2020-03-23 点击次数：
所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：med. image anal.
摘要：alzheimer's disease (ad) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. currently, many multi-task learning approaches have been proposed to predict the disease progression at the early stage using longitudinal data, with each task corresponding to a particular time point. however, the underlying association among different time points in disease progression is still under-explored in previous studies. to this end, we propose a multi-task exclusive relationship learning model to automatically capture the intrinsic relationship among tasks at different time points for estimating clinical measures based on longitudinal imaging data. the proposed method can select the most discriminative features for different tasks and also model the intrinsic relatedness among different time points, by utilizing an exclusive lasso regularization and a relationship induced regularization. specifically, the exclusive lasso regularization enables partial group structure feature selection among the longitudinal data, while the relationship induced regularization efficiently introduces the relationship information from data to guide knowledge transfer. we further develop an efficient optimization algorithm to solve the proposed objective function. extensive experiments on both synthetic and real datasets demonstrate the effectiveness of our proposed method. in comparison with several state-of-the-art methods, our proposed method can achieve promising performance for cognitive status prediction and also can help discover disease-related biomarkers. © 2019 elsevier b.v.
issn号：1361-8415
是否译文：否
发表时间：2019-04-01
合写作者：wang, mingliang,shen, dinggang,liu, mingxia
通讯作者：张道强
发表时间：2019-04-01
发表刊物：med. image anal.
摘要：alzheimer's disease (ad) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. currently, many multi-task learning approaches have been proposed to predict the disease progression at the early stage using longitudinal data, with each task corresponding to a particular time point. however, the underlying association among different time points in disease progression is still under-explored in previous studies. to this end, we propose a multi-task exclusive relationship learning model to automatically capture the intrinsic relationship among tasks at different time points for estimating clinical measures based on longitudinal imaging data. the proposed method can select the most discriminative features for different tasks and also model the intrinsic relatedness among different time points, by utilizing an exclusive lasso regularization and a relationship induced regularization. specifically, the exclusive lasso regularization enables partial group structure feature selection among the longitudinal data, while the relationship induced regularization efficiently introduces the relationship information from data to guide knowledge transfer. we further develop an efficient optimization algorithm to solve the proposed objective function. extensive experiments on both synthetic and real datasets demonstrate the effectiveness of our proposed method. in comparison with several state-of-the-art methods, our proposed method can achieve promising performance for cognitive status prediction and also can help discover disease-related biomarkers. © 2019 elsevier b.v.
issn号：1361-8415
是否译文：否
发表时间：2019-04-01
合写作者：wang, mingliang,shen, dinggang,liu, mingxia
通讯作者：张道强
发表时间：2019-04-01
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