Journal
MEDICAL IMAGE ANALYSIS
Volume 38, Issue -, Pages 205-214Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2015.10.008
Keywords
Alzheimer's disease; Feature selection; Sparse coding; Manifold learning; MCI conversion
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Funding
- NIH grants [EB006733, EB008374, EB009634, MH100217, AG041721, AG042599]
- ICT RAMP
- D program of MSIP/IITP [B0101-15-0307]
- National Research Foundation of Korea (NRF) grant - Korea government [NRF-2015R1A2A1A05001867]
- National Natural Science Foundation of China [61263035, 61573270]
- Guangxi Natural Science Foundation [2015 GXNSFCB139011]
- Guangxi 100 Plan
- National Research Foundation of Korea [2015R1C1A1A01052216, 2015R1A2A1A05001867] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an l(2,1)-norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods. (C) 2015 Elsevier B.V. All rights reserved.
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