Machine and Deep Learning in Oncology, Medical Physics and Radiology
Description of Machine and Deep Learning in Oncology, Medical Physics and Radiology
This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection.
Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.
About the Author
Issam El Naqa is founding Chair of Machine Learning Department and Associate Member of Radiation Oncology at Moffitt Cancer Center in Tampa, Florida.. He is board certified as a medical physicist by the American Board of Radiology.
Martin J Murphy is Professor Emeritus of radiation oncology at Virginia Commonwealth University (VCU), where he directed research into image-guided surgery and radiation therapy, employing principles of machine learning and neural networks. He received his PhD in physics from the University of Chicago.
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