Machine learning in radiation oncology: theory and applications

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised...

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Detalles Bibliográficos
Autores principales: El Naqa, Issam, Li, Ruijiang, Murphy, Martin J
Lenguaje:eng
Publicado: Springer 2015
Materias:
Health Physics and Radiation Effects
Acceso en línea:http://cds.cern.ch/record/2043017
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author El Naqa, Issam
Li, Ruijiang
Murphy, Martin J
author_facet El Naqa, Issam
Li, Ruijiang
Murphy, Martin J
author_sort El Naqa, Issam
collection CERN
description ​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided rad
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2015
publisher Springer
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spelling cern-20430172021-04-21T20:07:03Zhttp://cds.cern.ch/record/2043017engEl Naqa, IssamLi, RuijiangMurphy, Martin JMachine learning in radiation oncology: theory and applicationsHealth Physics and Radiation Effects​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radSpringeroai:cds.cern.ch:20430172015
spellingShingle Health Physics and Radiation Effects
El Naqa, Issam
Li, Ruijiang
Murphy, Martin J
Machine learning in radiation oncology: theory and applications
title Machine learning in radiation oncology: theory and applications
title_full Machine learning in radiation oncology: theory and applications
title_fullStr Machine learning in radiation oncology: theory and applications
title_full_unstemmed Machine learning in radiation oncology: theory and applications
title_short Machine learning in radiation oncology: theory and applications
title_sort machine learning in radiation oncology: theory and applications
topic Health Physics and Radiation Effects
url http://cds.cern.ch/record/2043017
work_keys_str_mv AT elnaqaissam machinelearninginradiationoncologytheoryandapplications
AT liruijiang machinelearninginradiationoncologytheoryandapplications
AT murphymartinj machinelearninginradiationoncologytheoryandapplications

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