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Deep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas

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dc.contributor.authorOh, B.-
dc.contributor.authorChu, Y.S.-
dc.contributor.authorLee, S.-
dc.contributor.authorLee, S.G.-
dc.contributor.authorChung, K.Y.-
dc.contributor.authorRoh, M.R.-
dc.contributor.authorSeo, K.D.-
dc.contributor.authorYang, S.-
dc.date.accessioned2023-07-12T02:49:45Z-
dc.date.available2023-07-12T02:49:45Z-
dc.date.created2023-07-28-
dc.date.issued2023-03-
dc.identifier.issn1605-7422-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/195408-
dc.description.abstractIn Asians, melanoma appears as pigmented lesions on the hands and feet, and is often diagnosed as acral malignant melanoma (ALM) in the late stage with a very poor prognosis. Among diverse clinical characteristics of melanoma, the presence of basement membrane involvement is one of the most important prognostic factors. However, there have been few studies reporting artificial intelligence for prediction of basement membrane involvement in ALMs beyond its diagnosis. Therefore, in this study, we present a deep learning model that predicts the basement membrane involvement of ALMs from dermoscopy images.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherSPIE-
dc.relation.isPartOfProgress in Biomedical Optics and Imaging - Proceedings of SPIE-
dc.relation.isPartOfProgress in Biomedical Optics and Imaging - Proceedings of SPIE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDeep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Dermatology (피부과학교실)-
dc.contributor.googleauthorOh, B.-
dc.contributor.googleauthorChu, Y.S.-
dc.contributor.googleauthorLee, S.-
dc.contributor.googleauthorLee, S.G.-
dc.contributor.googleauthorChung, K.Y.-
dc.contributor.googleauthorRoh, M.R.-
dc.contributor.googleauthorSeo, K.D.-
dc.contributor.googleauthorYang, S.-
dc.identifier.doi10.1117/12.2648034-
dc.relation.journalcodeJ02551-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordBreslow thickness-
dc.subject.keywordDeep learning-
dc.subject.keywordMelanoma-
dc.subject.keywordSkin cancer-
dc.subject.keywordTumor depth-
dc.contributor.alternativeNameOh, Byung Ho-
dc.contributor.affiliatedAuthorOh, B.-
dc.identifier.scopusid2-s2.0-85159725880-
dc.identifier.wosid001012400300012-
dc.citation.volume12352-
dc.identifier.bibliographicCitationProgress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol.12352, 2023-03-
dc.identifier.rimsid80407-
dc.type.rimsART-
dc.description.journalClass1-
dc.description.journalClass1-
dc.subject.keywordAuthorArtificial intelligence-
dc.subject.keywordAuthorBreslow thickness-
dc.subject.keywordAuthorDeep learning-
dc.subject.keywordAuthorMelanoma-
dc.subject.keywordAuthorSkin cancer-
dc.subject.keywordAuthorTumor depth-
dc.type.docTypeConference paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.relation.journalWebOfScienceCategoryDermatology-
dc.relation.journalWebOfScienceCategoryMedicine, Research & Experimental-
dc.relation.journalWebOfScienceCategorySurgery-
dc.relation.journalResearchAreaDermatology-
dc.relation.journalResearchAreaResearch & Experimental Medicine-
dc.relation.journalResearchAreaSurgery-
dc.identifier.articleno123520D-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Dermatology (피부과학교실) > 1. Journal Papers

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