Overview
- Provides a complete overview of the role of machine learning in radiation oncology and medical physics
- Covers the use of machine learning in quality assurance, computer-aided detection, image-guided radiotherapy, respiratory motion management, and outcome prediction
- Presents important relevant background information
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (18 chapters)
-
Front Matter
-
Machine Learning for Computer-Aided Detection
-
Front Matter
-
-
Machine Learning for Treatment Planning
-
Front Matter
-
-
Machine Learning Delivery and Motion Management
-
Front Matter
-
-
Machine Learning for Quality Assurance
-
Front Matter
-
Keywords
About this book
​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.
Editors and Affiliations
Bibliographic Information
Book Title: Machine Learning in Radiation Oncology
Book Subtitle: Theory and Applications
Editors: Issam El Naqa, Ruijiang Li, Martin J. Murphy
DOI: https://doi.org/10.1007/978-3-319-18305-3
Publisher: Springer Cham
eBook Packages: Medicine, Medicine (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Softcover ISBN: 978-3-319-35464-4Published: 12 October 2016
eBook ISBN: 978-3-319-18305-3Published: 19 June 2015
Edition Number: 1
Number of Pages: XIV, 336
Number of Illustrations: 60 b/w illustrations, 67 illustrations in colour