Taschenbuch. Condition: 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.
Language: English
Published by Springer International Publishing, 2016
ISBN 10: 3319354647 ISBN 13: 9783319354644
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: 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.
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. reprint edition. 350 pages. 9.25x6.10x0.83 inches. In Stock.
Seller: Brook Bookstore On Demand, Napoli, NA, Italy
Condition: new. Questo è un articolo print on demand.
Language: English
Published by Springer International Publishing Okt 2016, 2016
ISBN 10: 3319354647 ISBN 13: 9783319354644
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: 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.
Language: English
Published by Springer International Publishing, 2016
ISBN 10: 3319354647 ISBN 13: 9783319354644
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a complete overview of the role of machine learning in radiation oncology and medical physicsCovers the use of machine learning in quality assurance, computer-aided detection, image-guided radiotherapy, respiratory motion management, and .
Language: English
Published by Springer, Springer Okt 2016, 2016
ISBN 10: 3319354647 ISBN 13: 9783319354644
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 352 pp. Englisch.