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dc.contributor.author문영식-
dc.date.accessioned2022-08-22T23:56:49Z-
dc.date.available2022-08-22T23:56:49Z-
dc.date.issued2021-07-
dc.identifier.citation대한전자공학회 학술대회. 2021-06 2021(06):2392-2395en_US
dc.identifier.urihttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10591779-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/172535-
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.en_US
dc.description.sponsorship본 연구는 과학기술정통신부 및 정보통신기획평가원 의 SW 중심대학지원사업의 연구결과로 수행되었으며 (2018-0-00192) 연구 지원에 감사드립니다. 이 논문은 2021년도 정부(과학기술정보통신부)의 재 원으로 정보통신기획평가원의 지원을 받아 수행된 연 구임 (No.2020-0-01343, 인공지능융합연구센터지 원(한양대학교 ERICA))en_US
dc.language.isoko_KRen_US
dc.publisher대한전자공학회en_US
dc.title다중 작업 학습 기반의 피부 병변 분류 방법en_US
dc.title.alternativeMultitask Learning Based Skin Lesion Classification Methoden_US
dc.typeArticleen_US
dc.relation.page2392-2395-
dc.contributor.googleauthorPark, Kyung Ri-
dc.contributor.googleauthorKwon, Yong Woo-
dc.contributor.googleauthorKim, Ji Hoon-
dc.contributor.googleauthorKim, Hae Moon-
dc.contributor.googleauthorSuh, Ji Won-
dc.contributor.googleauthorKang, Kyung Won-
dc.contributor.googleauthorMoon, Young Shik-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF COMPUTING[E]-
dc.sector.departmentSCHOOL OF COMPUTER SCIENCE-
dc.identifier.pidysmoon-
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