Machine Learning in Radiation Oncology

Theory and Applications

Nonfiction, Science & Nature, Science, Physics, Radiation, Health & Well Being, Medical, Specialties, Radiology & Nuclear Medicine
Cover of the book Machine Learning in Radiation Oncology by , Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9783319183053
Publisher: Springer International Publishing Publication: June 19, 2015
Imprint: Springer Language: English
Author:
ISBN: 9783319183053
Publisher: Springer International Publishing
Publication: June 19, 2015
Imprint: Springer
Language: English

​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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

​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.

More books from Springer International Publishing

Cover of the book Peacebuilding by
Cover of the book Islamic Finance by
Cover of the book Image Processing and Communications Challenges 9 by
Cover of the book Synopsis of Pathophysiology in Nuclear Medicine by
Cover of the book Participation in Computing by
Cover of the book 100 Years of NCVO and Voluntary Action by
Cover of the book Supportive Cancer Care by
Cover of the book Surgery for Chest Wall Deformities by
Cover of the book Critical Dietary Factors in Cancer Chemoprevention by
Cover of the book Mapping Queer Space(s) of Praxis and Pedagogy by
Cover of the book Geometric Inequalities by
Cover of the book Rights and Wrongs by
Cover of the book Journalism and Social Media by
Cover of the book Social Management by
Cover of the book Proceedings of the Second International Conference on Mechatronics and Automatic Control by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy