Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment.
Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI)...
| Published in: | Brain Imaging & Behavior Vol. 10; no. 4; pp. 1148 - 1160 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Dec2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120010873&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120010873 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Dec2016 vid: 10 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120010873 120010873 NLM26572145 120010873 10.1007/s11682-015-9480-7 NLM26572145 120010873 ppf: 1148 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment. aug: au: Zu, Chen Jie, Biao Liu, Mingxia Chen, Songcan Shen, Dinggang Zhang, Daoqiang affil: Department of Computer Science and Engineering , Nanjing University of Aeronautics and Astronautics , Nanjing 210016 China sug: subj: Alzheimer's Disease Alzheimer's Disease Classification Brain Neuroradiography Methods ROC Curve Diagnostic Imaging Methods Human Information Science Methods Image Interpretation, Computer Assisted Methods Resource Databases Fludeoxyglucose F 18 Prognosis Disease Progression Radiopharmaceuticals Aged Tomography, Emission-Computed Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source Aged: 65+ years ab: Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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