연구성과로 돌아가기

2024 연구성과별 연구자 정보 (203 / 2344)

※ 현재 Web of Science 저자 정보만 집계되어 있습니다.
※ 컨트롤 + 클릭으로 열별 다중 정렬 가능합니다.
Excel 다운로드
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 Chang, Jee Suk Chang, JS 2 Yonsei Univ, Coll Med, Dept Radiat Oncol, Seoul, South Korea ABU-3301-2022 Chang, Jee Suk 0000-0001-7685-3382 Chang, Jee Suk 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, Kyubo Kim, K 3 Ewha Womans Univ, Coll Med, Dept Radiat Oncol, Seoul, South Korea R-8061-2019 Kim, Kyubo 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, Kyubo Kim, K 3 Seoul Natl Univ, Coll Med, Dept Radiat Oncol, Bundang Hosp, Seongnam, South Korea R-8061-2019 Kim, Kyubo 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, Jin Hee Kim, JH 4 Keimyung Univ, Dongsan Med Ctr, Sch Med, Dept Radiat Oncol, Daegu, 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, Tae Hyung Kim, TH 5 Eulji Univ, Sch Med, Nowon Eulji Med Ctr, Dept Radiat Oncol, Seoul, South Korea F-8620-2011 Kim, Jae Joon 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, Sungmin Kim, S 6 Dong A Univ, Dept Radiat Oncol, Dong A Univ Hosp, Coll Med, Pusan, South Korea IXD-7702-2023 KIM, MINJI 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 Cha, Hyejung Cha, H 7 Yonsei Univ, Wonju Coll Med, Dept Radiat Oncol, Wonju, 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, Oyeon Cho, O 8 Ajou Univ, Sch Med, Dept Radiat Oncol, Suwon, 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 Choi, Jin Hwa Choi, JH 9 Chung Ang Univ Hosp, Dept Radiat Oncol, Seoul, South Korea LWZ-8057-2024 CHOI, JIN HWA 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, Myungsoo Kim, M 10 Catholic Univ Korea, Incheon St Marys Hosp, Coll Med, Dept Radiat Oncol, Seoul, South Korea 0000-0002-0651-549X Kim, Myungsoo 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, Juree Kim, J 11 교신저자 CHA Univ, Ilsan CHA Med Ctr, Dept Radiat Oncol, Sch Med, Goyang, 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, Tae Gyu Kim, TG 12 Sungkyunkwan Univ, Samsung Changwon Hosp, Dept Radiat Oncol, Sch Med, Chang Won, South Korea AAL-8552-2021 Kim, Tae 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 Yeo, Seung-Gu Yeo, SG 13 Soonchunhyang Univ, Coll Med, Soonchunhyang Univ Hosp, Dept Radiat Oncol, Bucheon, South Korea AGU-7265-2022 Yeo, Seung-Gu 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 Chang, Ah Ram Chang, AR 14 Soonchunhyang 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 Ahn, Sung-Ja Ahn, SJ 15 Chonnam Natl Univ, Med Sch, Dept Radiat Oncol, Gwangju, South Korea jinsung@yuhs.ac.kr;radiat@snu.ac.kr;jinsung@yuhs.ac;
페이지 이동: