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College of Engineering(공과대학)Dept. of Nuclear and Quantum Engineering(원자력및양자공학과)NE-Conference Papers(학술회의논문)

Patient-Specific Deep Learning Model for Clinical Target Volume Delineation on Daily CBCT of Breast Cancer Patients based on Intentional Deep Overfit Learning (IDOL) Framework

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dc.contributor.authorHwang, Joonilko
dc.contributor.authorChun, Jaeheeko
dc.contributor.authorCho, Seo Heeko
dc.contributor.authorCho, Seungryongko
dc.contributor.authorKim, Jin Sungko
dc.date.accessioned2024-03-12T07:00:56Z-
dc.date.available2024-03-12T07:00:56Z-
dc.date.created2024-03-12-
dc.date.issued2023-10-02-
dc.identifier.citation65th Annual Meeting, ASTRO 2023-
dc.identifier.urihttp://hdl.handle.net/10203/318524-
dc.publisherAmerican Society for Radiation Oncology-
dc.titlePatient-Specific Deep Learning Model for Clinical Target Volume Delineation on Daily CBCT of Breast Cancer Patients based on Intentional Deep Overfit Learning (IDOL) Framework-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationname65th Annual Meeting, ASTRO 2023-
dc.identifier.conferencecountryUS-
dc.contributor.localauthorCho, Seungryong-
dc.contributor.nonIdAuthorChun, Jaehee-
dc.contributor.nonIdAuthorCho, Seo Hee-
dc.contributor.nonIdAuthorKim, Jin Sung-
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NE-Conference Papers(학술회의논문)
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