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Machine Learning in Radiation Oncology: Theory and Applications

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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.

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Detail Information

Series Title

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Call Number

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Publisher

Cham : Springer Cham., 2015

Collation

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Language

English

ISBN/ISSN

978-3-319-18305-3

Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Radiology, Medical Physics

Specific Detail Info

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Statement of Responsibility

Issam El Naqa, Ruijiang Li, Martin J. Murphy

Other Information

Cataloger

Kholif Basri

Source

https://link.springer.com/book/10.1007/978-3-319-18305-3

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