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2024 연구성과별 연구자 정보 (204 / 2344)

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Document Title Author Full Name Author Short Name Index Corresponding Address ResearcherID ResearcherID Author Name ORCID ORCID Author Name Related Email
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 Choi, Jinhyun Choi, J 16 Jeju Univ, Coll Med, Jeju Natl Univ Hosp, Dept Radiat Oncol, Jeju, South Korea jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Kang, Ki Mun Kang, KM 17 Gyeongsang Natl Univ, Changwon Hosp, Coll Med, Jinju, South Korea AAA-3684-2022 Kang, Kimun jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Kwon, Jeanny Kwon, J 18 Chungnam Natl Univ, Sch Med, Dept Radiat Oncol, Daejeon, South Korea Q-3852-2019 Kwon, Jeong jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Koo, Taeryool Koo, T 19 Hallym Univ, Sacred Heart Hosp, Coll Med, Dept Radiat Oncol, Anyang, South Korea jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Kim, Mi Young Kim, MY 20 Kyungpook Natl Univ, Chilgok Hosp, Dept Radiat Oncol, Daegu, South Korea ABC-4815-2020 Kim, Haeyoung jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Choi, Seo Hee Choi, SH 21 Yonsei Univ, Coll Med, Yongin Severance Hosp, Dept Radiat Oncol, Yongin, South Korea AFL-9783-2022 Choi, Seo-hee 0000-0002-4083-6414 Choi, Seo Hee jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Jeong, Bae Kwon Jeong, BK 22 Gyeongsang Natl Univ, Coll Med, Dept Radiat Oncol, Gyeongsang Natl Univ Hosp, Jinju, South Korea jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Jang, Bum-Sup Jang, BS 23 Seoul Natl Univ, Coll Med, Dept Radiat Oncol, Seoul, South Korea jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Jo, In Young Jo, IY 24 Soonchunhyang Univ Hosp, Dept Radiat Oncol, Cheonan, South Korea jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Lee, Hyebin Lee, H 25 Sungkyunkwan Univ, Sch Med, Kangbuk Samsung Hosp, Dept Radiat Oncol, Seoul, South Korea J-2154-2015 Lee, Hye jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Kim, Nalee Kim, N 26 Sungkyunkwan Univ, Sch Med, Samsung Med Ctr, Dept Radiat Oncol, Seoul, South Korea KGK-4760-2024 Kim, Nalee jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Park, Hae Jin Park, HJ 27 Hanyang Univ, Coll Med, Dept Radiat Oncol, Seoul, South Korea jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Im, Jung Ho Im, JH 28 CHA Univ, Sch Med, CHA Bundang Med Ctr, Dept Radiat Oncol, Seongnam, South Korea jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Lee, Sea-Won Lee, SW 29 Catholic Univ Korea, Coll Med, Eunpyeong St Marys Hosp, Dept Radiat Oncol, Seoul, South Korea jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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 Cho, Yeona Cho, Y 30 Yonsei Univ, Coll Med, Gangnam Severance Hosp, Dept Radiat Oncol, Seoul, South Korea 0000-0002-1202-0880 cho, Yeona jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
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