Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis.
Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract featur...
| Published in: | Brain Imaging & Behavior Vol. 10; no. 3; pp. 818 - 829 |
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| Main Authors: | , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Sep2016
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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=117723667&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117723667 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Sep2016 vid: 10 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 117723667 117723667 NLM26254746 117723667 10.1007/s11682-015-9430-4 NLM26254746 PMC4747862 117723667 ppf: 818 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis. aug: au: Zhu, Xiaofeng Suk, Heung-Il Lee, Seong-Whan Shen, Dinggang affil: Department of Radiology and BRIC , The University of North Carolina at Chapel Hill , Chapel Hill USA sug: subj: Brain Alzheimer's Disease Tomography, Emission-Computed Methods Magnetic Resonance Imaging Methods Alzheimer's Disease Classification Middle Age Male Aged, 80 and Over Female Psychological Tests Neuropsychological Tests Regression Alzheimer's Disease Physiopathology Algorithms Aged Data Collection Brain Physiopathology Scales Funding Source Human Middle Aged: 45-64 years Aged, 80 & over Aged: 65+ years Male Female ab: Fusing information from different imaging modalities is crucial for more accurate identification of the brain state because imaging data of different modalities can provide complementary perspectives on the complex nature of brain disorders. However, most existing fusion methods often extract features independently from each modality, and then simply concatenate them into a long vector for classification, without appropriate consideration of the correlation among modalities. In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation, by canonical correlation analysis. We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. In our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we use Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer's disease diagnosis. The experimental results showed that the proposed canonical feature selection method helped enhance the performance of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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