Machine Learning in Radiation Oncology
Theory and Applications
Editors: El Naqa, Issam, Li, Ruijiang, Murphy, Martin J. (Eds.)
Free Preview- 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
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- About this book
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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.
- Table of contents (18 chapters)
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What Is Machine Learning?
Pages 3-11
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Computational Learning Theory
Pages 13-20
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Machine Learning Methodology
Pages 21-39
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Performance Evaluation in Machine Learning
Pages 41-56
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Informatics in Radiation Oncology
Pages 57-70
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Table of contents (18 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Machine Learning in Radiation Oncology
- Book Subtitle
- Theory and Applications
- Editors
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- Issam El Naqa
- Ruijiang Li
- Martin J. Murphy
- Copyright
- 2015
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing Switzerland
- eBook ISBN
- 978-3-319-18305-3
- DOI
- 10.1007/978-3-319-18305-3
- Hardcover ISBN
- 978-3-319-18304-6
- Softcover ISBN
- 978-3-319-35464-4
- Edition Number
- 1
- Number of Pages
- XIV, 336
- Number of Illustrations
- 60 b/w illustrations, 67 illustrations in colour
- Topics
