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

Verlag: Springer, 2016

reprint edition. 350 pages. 9.25x6.10x0.83 inches. In Stock. Bestandsnummer des Verkäufers __3319354647

Inhaltsangabe:

​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.

Von der hinteren Coverseite: 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.

Bibliografische Details

Titel: Machine Learning in Radiation Oncology: ...
Verlag: Springer
Erscheinungsdatum: 2016
Einband: Paperback
Zustand: Brand New