Sparse shared structure based multi-task learning for MRI based cognitive performance prediction of Alzheimer's disease

被引:41
|
作者
Cao, Peng [1 ,2 ]
Shan, Xuanfeng [1 ]
Zhao, Dazhe [2 ]
Huang, Min [3 ]
Zaiane, Osmar [4 ]
机构
[1] Northeastern Univ, Comp Sci & Engn, Shenyang, Liaoning, Peoples R China
[2] Northeastern Univ, Minist Educ, Key Lab Med Image Comp, Shenyang, Liaoning, Peoples R China
[3] Northeastern Univ, Informat Sci & Engn, Shenyang, Liaoning, Peoples R China
[4] Univ Alberta, Comp Sci, Edmonton, AB, Canada
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
Alzheimer's disease; Multi-task learning; Proximal gradient; Regression; Biomarker discovery; FEATURE-SELECTION; REGRESSION; CLASSIFICATION; IMPAIRMENT; DIAGNOSIS; PROGRESSION;
D O I
10.1016/j.patcog.2017.07.018
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Alzheimer's disease (AD), the most common form of dementia, not only causes progressive impairment of memory and other cognitive functions of patients, but also becomes the substantial financial burden to the health care system. There is thus an urgent need to (1) accurately predict the cognitive performance of the disease, and (2) identify potential MRI-related biomarkers most predictive of the estimation of cognitive outcomes. In this paper, we develop a novel multi-task learning formulation to explore the correlation existing in Magnetic Resonance Imaging (MRI) and cognitive measures by a mixed norm incorporating a hierarchical group sparsity and shared subspace uncovering regularization, to learn a shared structure from multiple related tasks with considering implicit shared subspace structure and explicit subset of features as well as Region-of-Interests (ROIs) simultaneously. An efficient alternating optimization algorithm is derived to solve the proposed non-convex and non-smooth objective formulation. We comprehensively evaluate the proposed algorithm for the cognitive outcome prediction including all subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The experimental results not only demonstrate the proposed method has superior performance over multiple state-of-the-art comparable approaches, but also identifies cognition-relevant MRI biomarkers that are consistent with prior knowledge. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:219 / 235
页数:17
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