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다중 작업 학습 기반의 피부 병변 분류 방법

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dc.contributor.author박경리-
dc.contributor.author권용우-
dc.contributor.author김지훈-
dc.contributor.author김해문-
dc.contributor.author서지원-
dc.contributor.author강경원-
dc.contributor.author문영식-
dc.date.accessioned2023-09-04T05:44:09Z-
dc.date.available2023-09-04T05:44:09Z-
dc.date.issued2021-07-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114962-
dc.description.abstractSkin lesions have a high misdiagnosis rate due to a wide variety of forms. Recently, a deep learning based skin lesion classification method is difficult to classify due to hair and fuzzy boundaries of skin lesions. In this paper, we propose a network for classifying skin lesions and segmenting skin lesion regions using a multitask learning method. Experimentally, the result shows that the performance of our method has been improved by 2.48 % over the previous method.-
dc.format.extent4-
dc.language한국어-
dc.language.isoKOR-
dc.publisher대한전자공학회-
dc.title다중 작업 학습 기반의 피부 병변 분류 방법-
dc.title.alternativeMultitask Learning Based Skin Lesion Classification Method-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.bibliographicCitation2021년 대한전자공학회 하계학술대회 논문집, pp 2392 - 2395-
dc.citation.title2021년 대한전자공학회 하계학술대회 논문집-
dc.citation.startPage2392-
dc.citation.endPage2395-
dc.type.docTypeProceeding-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassother-
dc.identifier.urlhttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10591779-
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