This extensively revised and updated second edition of the book offers a comprehensive overview of machine learning and deep learning in the fields of oncology, medical physics, and radiology. It covers the fundamental theory, methods, and practical applications in these domains. The introductory section provides an explanation of machine and deep learning, explores learning methods, evaluates performance, and discusses software tools and data security.
The subsequent sections focus on the use of machine and deep learning for medical image analysis, treatment planning and delivery, and modeling outcomes and decision support. Each chapter includes resources for specific applications, and relevant software code is included for illustrative purposes. This book is a valuable resource for students and residents in medical physics, radiology, and oncology, as well as experienced practitioners, researchers, and members of the applied machine learning community.
2nd Edition
Description:
Discover the extensively revised and updated second edition of this comprehensive book, delving into the role of machine learning and deep learning in the fields of oncology, medical physics, and radiology. Gain in-depth knowledge of fundamental theory, methods, and practical applications across these domains. The introductory section provides insights into machine and deep learning, learning methods, performance evaluation, software tools, and data security.
Subsequent sections focus on utilizing machine and deep learning for medical image analysis, treatment planning and delivery, as well as outcomes modeling and decision support. Each chapter includes valuable resources and illustrative software code. This book is a valuable resource for medical physics, radiology, and oncology students, residents, practitioners, researchers, and members of applied machine learning communities.
From the Back Cover:
This extensively revised and updated second edition of the book provides a comprehensive overview of the roles of machine learning and deep learning in the fields of oncology, medical physics, and radiology. It covers the fundamental theories, methods, and practical applications within these domains.
The introductory section presents an explanation of machine and deep learning, offers a review of learning methods, discusses performance evaluation, and explores software tools and data protection. Subsequent sections delve into the specific applications of machine and deep learning, including medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Each chapter includes resources tailored to different applications, and relevant software code is embedded for illustrative purposes.
This book is an invaluable resource for students and residents in medical physics, radiology, and oncology, as well as for experienced practitioners, researchers, and members of the applied machine learning communities.
About the Author:
Issam El Naqa, founding Chair of the Machine Learning Department and Associate Member of Radiation Oncology at Moffitt Cancer Center in Tampa, Florida, is a board-certified medical physicist. Dr. El Naqa's extensive academic journey includes a BSc and MSc in Electrical and Communication Engineering from the University of Jordan, a PhD in Electrical and Computer Engineering from the Illinois Institute of Technology, and an MA in Biology Science from Washington University.
He has held positions at McGill University Health Centre, the University of Michigan at Ann Arbor, and has published over 200 peer-reviewed journal publications and 4 edited textbooks in machine learning, data analytics, and oncology outcomes modeling. He is a member and fellow of several academic and professional societies, including AAPM and IEEE.
Martin J Murphy, Professor Emeritus of Radiation Oncology at Virginia Commonwealth University, brings his expertise in machine learning and neural networks to the field of image-guided surgery and radiation therapy.
With a PhD in physics from the University of Chicago, Dr. Murphy has conducted research in various areas before joining the original development team for the CyberKnife. He has led projects applying robotics and machine learning to image guidance and has been the principal investigator for numerous grants.
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