Main description:

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.

Contents:

Part I. Introduction
1. What are Machine and Deep Learning?2. Computational Learning Basics3. Overview of Conventional Machine Learning Methods4. Overview of Deep Machine Learning Methods5. Quantum Computing for Machine Learning6. Performance Evaluation7. Software Tools for Machine and Deep learning8. Data sharing, protection and bioethics
Part II. Machine Learning for Medical Image Analysis
9. Detection of Cancer Lesions from Imaging10. Diagnosis of Malignant and Benign Tumours 11. Auto-contouring for image-guidance and treatment planning
Part III. Machine Learning for Treatment planning & Delivery
12. Quality Assurance and error prediction 13. Knowledge-based treatment planning 14. Intelligent respiratory motion management
Part IV. Machine Learning for Outcomes Modeling and Decision Support
15. Prediction of oncology treatment outcomes 16. Radiomics and radiogenomics 17. Modelling of Radiotherapy Response (TCP/NTCP)18. Smart adaptive treatment strategies 19. Machine learning in clinical trials