Machine Learning in Radiation Oncology : Theory and Applications

Issam El Naqa

ISBN 10: 3319354647 / ISBN 13: 9783319354644

Published by Springer International Publishing, 2016

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783319354644

Bibliographic Details

• Title: Machine Learning in Radiation Oncology : Theory and Applications
• Publisher: Springer International Publishing
• Publication Date: 2016
• Language: English
• ISBN 10: 3319354647
• ISBN 13: 9783319354644
• Binding: Taschenbuch
• Condition: Neu

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.