Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Machine Learning in Radiation Oncology | Theory and Applications | Issam El Naqa (u. a.) | Taschenbuch | xiv | Englisch | 2016 | Springer | EAN 9783319354644 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. 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.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 160,74
Anzahl: 3 verfügbar
In den WarenkorbZustand: New. pp. 300.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 148,58
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. reprint edition. 350 pages. 9.25x6.10x0.83 inches. In Stock.
Sprache: Englisch
Verlag: Springer, Berlin, Springer International Publishing, Springer, 2015
ISBN 10: 3319183044 ISBN 13: 9783319183046
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. 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.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 194,44
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
Anbieter: moluna, Greven, Deutschland
EUR 205,53
Anzahl: Mehr als 20 verfügbar
In den WarenkorbKartoniert / Broschiert. Zustand: New. Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinicProvides perspectives from artificial .
Sprache: Englisch
Verlag: Elsevier Science Publishing Co Inc Dez 2023, 2023
ISBN 10: 0128220007 ISBN 13: 9780128220009
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology.