Machine Learning in Radiation Oncology - Theory and Applications (Hardcover, 2015 ed.)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	July 2015
First published:	2015
Editors:	Issam El Naqa • Ruijiang Li • Martin J. Murphy
Dimensions:	235 x 155 x 20mm (L x W x T)
Format:	Hardcover - Cloth over boards
Pages:	336
Edition:	2015 ed.
ISBN-13:	978-3-319-18304-6
Categories:	Books > Medicine > Other branches of medicine > Medical imaging > Radiology Books > Medicine > Clinical & internal medicine > Diseases & disorders > Oncology > Radiotherapy
LSN:	3-319-18304-4
Barcode:	9783319183046

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