Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 문영식 | - |
dc.date.accessioned | 2022-04-01T02:38:39Z | - |
dc.date.available | 2022-04-01T02:38:39Z | - |
dc.date.issued | 2021-11 | - |
dc.identifier.citation | 대한전자공학회 학술대회. 2021-11 2021(11):482-486 | en_US |
dc.identifier.uri | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11027634 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/169626 | - |
dc.description.abstract | In medical data, the distance between the wound and the imaging device is not constant, so there is a problem that the deep learning model becomes sensitive to changes in resolution. To solve this problem, in this paper, we propose a network that is robust to resolution changes through multiple resolution input. Experimentally, the proposed method is more robust to resolution changes than the existing method, and shows 1.48% higher performance. | en_US |
dc.description.sponsorship | 본 연구는 과학기술정통신부 및 정보통신기획평가원의 SW 중심대학지원사업의 연구결과로 수행되었으며 (2018-0-00192) 연구 지원에 감사드립니다. 이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임 (No.2020-0-01343, 인공지능융합연구센터지원(한양대학교 ERICA)) | en_US |
dc.language.iso | ko_KR | en_US |
dc.publisher | 대한전자공학회 | en_US |
dc.title | 다중 해상도를 이용한 딥러닝 기반 창상 분류 방법 | en_US |
dc.title.alternative | Deep Learning Based Wound Classification Method Using Multiple Resolutions | en_US |
dc.type | Article | en_US |
dc.relation.page | 482-486 | - |
dc.contributor.googleauthor | 박, 경리 | - |
dc.contributor.googleauthor | 김, 지훈 | - |
dc.contributor.googleauthor | 김, 해문 | - |
dc.contributor.googleauthor | 차, 지환 | - |
dc.contributor.googleauthor | 유, 희진 | - |
dc.contributor.googleauthor | 문, 영식 | - |
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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