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] (The Breast (2024) 73, (S0960977623007257), (10.1016/j.breast.2023.103599))

Min Seo Choi, Jee Suk Chang, Kyubo Kim, Jin Hee Kim, Tae Hyung Kim, Sungmin Kim, Hyejung Cha, Oyeon Cho, Jin Hwa Choi, Myungsoo Kim, Juree Kim, Tae Gyu Kim, Seung Gu Yeo, Ah Ram Chang, Sung Ja Ahn, Jinhyun Choi, Ki Mun Kang, Jeanny Kwon, Taeryool Koo, Mi Young KimSeo Hee Choi, Bae Kwon Jeong, Bum Sup Jang, In Young Jo, Hyebin Lee, Nalee Kim, Hae Jin Park, Jung Ho Im, Sea Won Lee, Yeona Cho, Sun Young Lee, Ji Hyun Chang, Jaehee Chun, Eung Man Lee, Jin Sung Kim, Kyung Hwan Shin, Yong Bae Kim

Research output: Contribution to journalComment/debate

Abstract

The 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.

Original languageEnglish
Article number103624
JournalBreast
Volume74
DOIs
StatePublished - Apr 2024

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