Robust multi-label transfer feature learning for early diagnosis of Alzheimer's disease.
Transfer learning has been successfully used in the early diagnosis of Alzheimer's disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source...
| Published in: | Brain Imaging & Behavior Vol. 13; no. 1; pp. 138 - 154 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Feb2019
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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=135233780&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135233780 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Feb2019 vid: 13 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135233780 135233780 NLM29589326 135233780 10.1007/s11682-018-9846-8 NLM29589326 135233780 ppf: 138 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Robust multi-label transfer feature learning for early diagnosis of Alzheimer's disease. aug: au: Cheng, Bo Liu, Mingxia Zhang, Daoqiang Shen, Dinggang Alzheimer's Disease Neuroimaging Initiative affil: Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, 404100, Chongqing, China sug: subj: Alzheimer's Disease Diagnosis Diagnosis, Computer Assisted Methods Early Diagnosis Information Science Methods Sensitivity and Specificity Human Prospective Studies Magnetic Resonance Imaging Brain Validation Studies Comparative Studies Evaluation Research Multicenter Studies Clinical Assessment Tools ab: Transfer learning has been successfully used in the early diagnosis of Alzheimer's disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several 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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