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Deep Learning in Radiation Oncology

DC FieldValueLanguage
dc.contributor.author김진성-
dc.date.accessioned2021-01-19T07:51:54Z-
dc.date.available2021-01-19T07:51:54Z-
dc.date.issued2020-09-
dc.identifier.issn2508-4445-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/181355-
dc.description.abstractDeep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherKorean Society of Medical Physics-
dc.relation.isPartOfProgress in Medical Physics-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDeep Learning in Radiation Oncology-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiation Oncology (방사선종양학교실)-
dc.contributor.googleauthorWonjoong Cheon-
dc.contributor.googleauthorHaksoo Kim-
dc.contributor.googleauthorJinsung Kim-
dc.identifier.doi10.14316/pmp.2020.31.3.111-
dc.contributor.localIdA04548-
dc.relation.journalcodeJ03591-
dc.identifier.eissn2508-4453-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordDeep learning-
dc.subject.keywordMachine learning-
dc.subject.keywordRadiation oncology-
dc.contributor.alternativeNameKim, Jinsung-
dc.contributor.affiliatedAuthor김진성-
dc.citation.volume31-
dc.citation.number3-
dc.citation.startPage111-
dc.citation.endPage123-
dc.identifier.bibliographicCitationProgress in Medical Physics, Vol.31(3) : 111-123, 2020-09-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers

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