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Machine Learning in Radiation Oncology

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Machine Learning in Radiation Oncology

Theory and Applications

Issam El Naqa, Ruijiang Li, Martin J. Murphy

Springer
Publication date : 2015-06-19

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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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About the book

Author

Issam El Naqa , Ruijiang Li , Martin J. Murphy

Imprint

Springer

Collection

n.c

Publication date

2015-06-19

Pages

336 pages

Print ISBN

9783319183046

Language

English

Ebook informations

EAN PDF

9783319183053

Price

£74.50

Copy count 3

Print count 33

File size 12636 Ko

Informations accessibility

EAN EPUB

9783319183053

Price

£74.50

Copy count 3

Print count 33

File size 5028 Ko

Informations accessibility

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About author(s)

Issam El Naqa

Ruijiang Li

Martin J. Murphy

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