A Robust Deep Model for Improved Classification of AD/MCI Patients
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, Feng | - |
dc.contributor.author | Tran, Loc | - |
dc.contributor.author | Thung, Kim-Han | - |
dc.contributor.author | Ji, Shuiwang | - |
dc.contributor.author | Shen, Dinggang | - |
dc.contributor.author | Li, Jiang | - |
dc.date.accessioned | 2021-09-04T13:01:21Z | - |
dc.date.available | 2021-09-04T13:01:21Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2015-09 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/92584 | - |
dc.description.abstract | Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of a particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight coadaptation, which is a typical cause of overfitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multitask learning strategy into the deep learning framework. We applied the proposed method to the ADNI dataset, and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | REPRESENTATION | - |
dc.title | A Robust Deep Model for Improved Classification of AD/MCI Patients | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1109/JBHI.2015.2429556 | - |
dc.identifier.scopusid | 2-s2.0-84940975497 | - |
dc.identifier.wosid | 000360791200010 | - |
dc.identifier.bibliographicCitation | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.19, no.5, pp.1610 - 1616 | - |
dc.relation.isPartOf | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.title | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | - |
dc.citation.volume | 19 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1610 | - |
dc.citation.endPage | 1616 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.subject.keywordPlus | REPRESENTATION | - |
dc.subject.keywordAuthor | Alzheimer&apos | - |
dc.subject.keywordAuthor | s disease (AD) | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | early diagnosis | - |
dc.subject.keywordAuthor | magnetic resonance imaging (MRI) | - |
dc.subject.keywordAuthor | positron emission tomography (PET) | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.