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

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 journalArticlepeer-review

Fingerprint

Dive into the research topics of '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'. Together they form a unique fingerprint.

Engineering

Medicine and Dentistry

Nursing and Health Professions

Psychology