Machine Learning in Radiation Oncology: Theory and Applications
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.
*An electronic version of a printed book that can be read on a computer or handheld device designed specifically for this purpose.
Formats for this Ebook
|Required Software||Any PDF Reader, Apple Preview|
|Supported Devices||Windows PC/PocketPC, Mac OS, Linux OS, Apple iPhone/iPod Touch.|
|# of Devices||Unlimited|
|Flowing Text / Pages||Pages|
- PDF | 336 pages
- Springer; Softcover reprint of the original 1st ed. 2015 edition (November 18, 2016)
- New, Used & Rental Textbooks
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