BROWSE

Cited 0 times in Scipus Cited Count

Corrigendum to ‘Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study’ [The Breast 73 (2024) 103599]

DC Field Value Language
dc.contributor.authorChoi, MS-
dc.contributor.authorChang, JS-
dc.contributor.authorKim, K-
dc.contributor.authorKim, JH-
dc.contributor.authorKim, TH-
dc.contributor.authorKim, S-
dc.contributor.authorCha, H-
dc.contributor.authorCho, O-
dc.contributor.authorChoi, JH-
dc.contributor.authorKim, M-
dc.contributor.authorKim, J-
dc.contributor.authorKim, TG-
dc.contributor.authorYeo, SG-
dc.contributor.authorChang, AR-
dc.contributor.authorAhn, SJ-
dc.contributor.authorChoi, J-
dc.contributor.authorKang, KM-
dc.contributor.authorKwon, J-
dc.contributor.authorKoo, T-
dc.contributor.authorKim, MY-
dc.contributor.authorChoi, SH-
dc.contributor.authorJeong, BK-
dc.contributor.authorJang, BS-
dc.contributor.authorJo, IY-
dc.contributor.authorLee, H-
dc.contributor.authorKim, N-
dc.contributor.authorPark, HJ-
dc.contributor.authorIm, JH-
dc.contributor.authorLee, SW-
dc.contributor.authorCho, Y-
dc.contributor.authorLee, SY-
dc.contributor.authorChang, JH-
dc.contributor.authorChun, J-
dc.contributor.authorLee, EM-
dc.contributor.authorKim, JS-
dc.contributor.authorShin, KH-
dc.contributor.authorKim, YB-
dc.date.accessioned2024-04-04T06:27:29Z-
dc.date.available2024-04-04T06:27:29Z-
dc.date.issued2024-
dc.identifier.issn0960-9776-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/32446-
dc.description.abstractThe authors apologize for the oversight in presenting incomplete affiliations for author Jin Sung Kim. Jin Sung Kim is affiliated with the Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea, and Oncosoft Inc., Seoul, Republic of Korea. The authors would like to apologize for any inconvenience caused.-
dc.language.isoen-
dc.titleCorrigendum to ‘Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study’ [The Breast 73 (2024) 103599]-
dc.typeArticle-
dc.identifier.pmid38161095-
dc.contributor.affiliatedAuthorCho, O-
dc.type.localJournal Papers-
dc.identifier.doi10.1016/j.breast.2023.103624-
dc.citation.titleBreast (Edinburgh, Scotland)-
dc.citation.volume74-
dc.citation.date2024-
dc.citation.startPage103624-
dc.citation.endPage103624-
dc.identifier.bibliographicCitationBreast (Edinburgh, Scotland), 74. : 103624-103624, 2024-
dc.identifier.eissn1532-3080-
dc.relation.journalidJ009609776-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Radiation Oncology
Files in This Item:
38161095.pdfDownload
Show simple item record

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

  • License
    sherpa_romeo
    sherpa_juliet

OAK

Ajou University Medical Information & Media Center 164 Worldcup-ro Yeongtong-gu Suwon 16499 Korea / TEL : 031-219-5312
Copyright (c) Ajou University Medical Information & Media Center All Rights Reserved.
AJOU Open Repository는 국립중앙도서관 OAK 보급사업으로 구축되었습니다.

Browse

Guide 아주대학교 의학문헌정보센터