Machine Learning in Radiation Oncology: Theory and Applications
El Naqa, Issam (Editor) / Li, Ruijiang (Editor) / Murphy, Martin J. (Editor)
ISBN 10: 3319354647 ISBN 13: 9783319354644
Editorial: Springer, 2016
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Revaluation Books, Exeter, Reino Unido
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Descripción
Descripción:
reprint edition. 350 pages. 9.25x6.10x0.83 inches. In Stock. N° de ref. del artículo 3319354647
Sinopsis:
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.
De la contraportada: 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 radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
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Detalles bibliográficos
Título: Machine Learning in Radiation Oncology: ...
Editorial: Springer
Año de publicación: 2016
Encuadernación: Paperback
Condición: Brand New
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Machine Learning in Radiation Oncology | Theory and Applications
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Taschenbuch. Condición: Neu. Machine Learning in Radiation Oncology | Theory and Applications | Issam El Naqa (u. a.) | Taschenbuch | xiv | Englisch | 2016 | Springer | EAN 9783319354644 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 111861547
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Machine Learning in Radiation Oncology
Publicado por
Springer International Publishing Okt 2016, 2016
ISBN 10: 3319354647
ISBN 13: 9783319354644
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities. 352 pp. Englisch. Nº de ref. del artículo: 9783319354644
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Machine Learning in Radiation Oncology : Theory and Applications
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities. Nº de ref. del artículo: 9783319354644
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Machine Learning in Radiation Oncology: Theory and Applications
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Condición: New. pp. 350 Reprint edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26378362261
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