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.
- Hardback | 513 pages
- 155 x 235 x 27.94mm | 1,105g
- 10 Mar 2022
- Springer Nature Switzerland AG
- Cham, Switzerland
- 2nd ed. 2022
- 130 Tables, color; 112 Illustrations, color; 56 Illustrations, black and white; XVI, 513 p. 168 illus., 112 illus. in color.