Machine Learning in Radiation Oncology

Issam El Naqa

ISBN 10: 3319354647 / ISBN 13: 9783319354644

Editore: Springer International Publishing, Springer Nature Switzerland Okt 2016, 2016

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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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 352 pp. Englisch.

Codice articolo 9783319354644

Dati bibliografici

• Titolo: Machine Learning in Radiation Oncology
• Editore: Springer International Publishing, Springer Nature Switzerland Okt 2016
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3319354647
• ISBN 13: 9783319354644
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 18 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 18 millimetri di profondità
• Peso: 598 grammi

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Riassunto

?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.