Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 문영식 | - |
dc.date.accessioned | 2022-08-22T23:56:49Z | - |
dc.date.available | 2022-08-22T23:56:49Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.citation | 대한전자공학회 학술대회. 2021-06 2021(06):2392-2395 | en_US |
dc.identifier.uri | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10591779 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/172535 | - |
dc.description.abstract | Skin 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.sponsorship | 본 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.iso | ko_KR | en_US |
dc.publisher | 대한전자공학회 | en_US |
dc.title | 다중 작업 학습 기반의 피부 병변 분류 방법 | en_US |
dc.title.alternative | Multitask Learning Based Skin Lesion Classification Method | en_US |
dc.type | Article | en_US |
dc.relation.page | 2392-2395 | - |
dc.contributor.googleauthor | Park, Kyung Ri | - |
dc.contributor.googleauthor | Kwon, Yong Woo | - |
dc.contributor.googleauthor | Kim, Ji Hoon | - |
dc.contributor.googleauthor | Kim, Hae Moon | - |
dc.contributor.googleauthor | Suh, Ji Won | - |
dc.contributor.googleauthor | Kang, Kyung Won | - |
dc.contributor.googleauthor | Moon, Young Shik | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | SCHOOL OF COMPUTER SCIENCE | - |
dc.identifier.pid | ysmoon | - |
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