TY - BOOK
T1 - MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN RADIATION ONCOLOGY
T2 - A GUIDE FOR CLINICIANS
AU - Kang, John
AU - Rattay, Tim
AU - Rosenstein, Barry S.
N1 - Publisher Copyright:
© 2024 Elsevier Inc. All rights reserved.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85184313904
U2 - 10.1016/C2019-0-02588-5
DO - 10.1016/C2019-0-02588-5
M3 - Book
AN - SCOPUS:85184313904
BT - MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN RADIATION ONCOLOGY
PB - Elsevier
ER -