基于中心化自动加权多任务学习的早期轻度认知障碍诊断

doi:10.3969/j.issn.0258-8021.2019.06.002

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Abstract Mild cognitive impairment (MCI) is the early stage of Alzheimer′s disease (AD) and is the best time for the diagnosis of the disease. By using multi-modal data one can comprehensively analyze the condition of the disease, which is conducive to the accurate diagnosis of the disease. However, the existing methods cannot effectively analyze the relationship between multiple modal data at the same time, and cannot effectively combine the advantages between functional state data and structural state data. For this reason, this paper proposed an auto-weighted centralized multi-task learning method for the diagnosis of MCI. The method can simultaneously learn data of different modalities and effectively combine the advantages between data. Specifically, firstly, brain networks were constructed for functional state data rs-fMRI and structural state data DTI respectively. Secondly, a new multi-task feature learning model was designed based on multi-modal data. The balance between the importance of each task and the modalities was automatically learned, including the similarity and specificity between different modalities, to obtain stable and discriminative expression features. Finally, the selected features were input into support vector machine (SVM) model for classification diagnosis. The experiments in this paper are based on the public database of the ADNI (Alzheimer′s Disease Neuroimaging Initiative), including significant memory concern (SMC), early mild cognitive Impairment (EMCI), late mild cognitive impairment (LMCI) and normal control (NC). The method had four different types of data for NC vs. SMC, SMC vs. EMCI, SMC vs. LMCI and EMCI vs. LMCI. The diagnostic result was 76.67%, 79.07%, 80.56% and 74.29%, respectively. Compared with other traditional algorithms, the classification accuracy of the methods described in this paper was significantly improved. The experimental results showed that the proposed method could be effectively applied to the diagnosis and analysis of early mild cognitive impairment.

Key words： early mild cognitive impairment feature selection and classification multi-modal multi-task learning

Received: 10 December 2018

PACS:

R318

Corresponding Authors: E-mail: leiby@szu.edu.cn

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	Articles by authors
	Cheng Nina
	Xiao Xiaohua
	Hu Huoyou
	Yang Peng
	Wang Tianfu
	Lei Baiying

Cite this article:

Cheng Nina,Xiao Xiaohua,Hu Huoyou, et al. Auto-Weighted Centralized Multi-Task Learning for Early MildCognitive Impairment Diagnosis[J]. Chinese Journal of Biomedical Engineering, 2019, 38(6): 653-661.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2019.06.002 OR http://cjbme.csbme.org/EN/Y2019/V38/I6/653

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