다중 작업 학습 기반의 피부 병변 분류 방법
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박경리 | - |
dc.contributor.author | 권용우 | - |
dc.contributor.author | 김지훈 | - |
dc.contributor.author | 김해문 | - |
dc.contributor.author | 서지원 | - |
dc.contributor.author | 강경원 | - |
dc.contributor.author | 문영식 | - |
dc.date.accessioned | 2023-09-04T05:44:09Z | - |
dc.date.available | 2023-09-04T05:44:09Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114962 | - |
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. | - |
dc.format.extent | 4 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 대한전자공학회 | - |
dc.title | 다중 작업 학습 기반의 피부 병변 분류 방법 | - |
dc.title.alternative | Multitask Learning Based Skin Lesion Classification Method | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.bibliographicCitation | 2021년 대한전자공학회 하계학술대회 논문집, pp 2392 - 2395 | - |
dc.citation.title | 2021년 대한전자공학회 하계학술대회 논문집 | - |
dc.citation.startPage | 2392 | - |
dc.citation.endPage | 2395 | - |
dc.type.docType | Proceeding | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | other | - |
dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10591779 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG 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.