Learning using privileged information improves neuroimaging-based CAD of Alzheimer's disease: a comparative study.
The neuroimaging-based computer-aided diagnosis (CAD) for Alzheimer's disease (AD) has shown its effectiveness in recent years. In general, the multimodal neuroimaging-based CAD always outperforms the approaches based on a single modality. However, single-modal neuroimaging is more favored in clinic...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 7; pp. 1605 - 1617 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jul2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137162498&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137162498 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2019 vid: 57 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137162498 137162498 NLM31028606 137162498 10.1007/s11517-019-01974-3 NLM31028606 137162498 ppf: 1605 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Learning using privileged information improves neuroimaging-based CAD of Alzheimer's disease: a comparative study. aug: au: Li, Yan Meng, Fanqing Shi, Jun affil: Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China sug: subj: Image Processing, Computer Assisted Methods Alzheimer's Disease Neuroradiography Methods Human Aged, 80 and Over Middle Age Magnetic Resonance Imaging Algorithms Resource Databases Tomography, Emission-Computed Aged Validation Studies Comparative Studies Evaluation Research Multicenter Studies Questionnaires Scales Aged, 80 & over Middle Aged: 45-64 years Aged: 65+ years ab: The neuroimaging-based computer-aided diagnosis (CAD) for Alzheimer's disease (AD) has shown its effectiveness in recent years. In general, the multimodal neuroimaging-based CAD always outperforms the approaches based on a single modality. However, single-modal neuroimaging is more favored in clinical practice for diagnosis due to the limitations of imaging devices, especially in rural hospitals. Learning using privileged information (LUPI) is a new learning paradigm that adopts additional privileged information (PI) modality to help to train a more effective learning model during the training stage, but PI itself is not available in the testing stage. Since PI is generally related to the training samples, it is then transferred to the learned model. In this work, a LUPI-based CAD framework for AD is proposed. It can flexibly perform a classifier- or feature-level LUPI, in which the information is transferred from the additional PI modality to the diagnosis modality. A thorough comparison has been made among three classifier-level algorithms and five feature-level LUPI algorithms. The experimental results on the ADNI dataset show that all classifier-level and deep learning based feature-level LUPI algorithms can improve the performance of a single-modal neuroimaging-based CAD for AD by transferring PI. Graphical abstract Graphical abstract for the framework of the LUPI-based CAD for AD. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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