ENGLISH

Machine Learning in Radiation Oncology: Theory and Applications

By Issam El Naqa , Ruijiang Li , Martin J. Murphy (eds.)

Book information

Publisher
Springer International Publishing
Year
2015
ISBN
978-3-319-18304-6, 978-3-319-18305-3
DOI
10.1007/978-3-319-18305-3
Language
english
Format
PDF
Filesize
12 MB (12942208 bytes)
Edition
1
Pages
336\336
Time added
2015-07-18 18:00:00
Radiotherapy; Medical and Radiation Physics

Description

​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.

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