Machine Learning in Radiation Oncology

Issam El Naqa

ISBN 10: 3319354647 / ISBN 13: 9783319354644

Published by Springer International Publishing, Springer Nature Switzerland Okt 2016, 2016

About this Item

Neuware -¿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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 352 pp. Englisch.

Seller Inventory # 9783319354644

Bibliographic Details

• Title: Machine Learning in Radiation Oncology
• Publisher: Springer International Publishing, Springer Nature Switzerland Okt 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 3319354647
• ISBN 13: 9783319354644
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 18D millimeters 235 millimeters width by 155 millimeters height by 18 millimeters depth
• Weight: 598 grams

About this title

Synopsis

​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.