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Machine Learning in Radiation Oncology

Theory and Applications

  • Issam El Naqa
  • Ruijiang Li
  • Martin J. Murphy

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Issam El Naqa, Martin J. Murphy
      Pages 3-11
    3. Issam El Naqa
      Pages 13-20
    4. Sangkyu Lee, Issam El Naqa
      Pages 21-39
    5. Nathalie Japkowicz, Mohak Shah
      Pages 41-56
    6. Paul Martin Putora, Samuel Peters, Marc Bovet
      Pages 57-70
    7. Johan P. A. van Soest, Andre L. A. J. Dekker, Erik Roelofs, Georgi Nalbantov
      Pages 71-97
  3. Machine Learning for Computer-Aided Detection

    1. Front Matter
      Pages 99-99
    2. Juan Wang, Issam El Naqa, Yongyi Yang
      Pages 133-153
  4. Machine Learning for Treatment Planning

    1. Front Matter
      Pages 155-155
    2. Yaozong Gao, Yanrong Guo, Yinghuan Shi, Shu Liao, Jun Lian, Dinggang Shen
      Pages 157-192
    3. Issam El Naqa
      Pages 193-199
  5. Machine Learning Delivery and Motion Management

    1. Front Matter
      Pages 201-201
    2. Ruijiang Li
      Pages 225-234
  6. Machine Learning for Quality Assurance

    1. Front Matter
      Pages 235-235
    2. Ruijiang Li, Steve B. Jiang
      Pages 243-252
    3. Ruijiang Li
      Pages 253-260
  7. Machine Learning for Outcomes Modeling

  8. Back Matter
    Pages 325-336

About this book

Introduction

This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Keywords

Machine Learning Medical Physics Outcome Modelling Radiation Oncology Radiation Physics Treatment Planning

Editors and affiliations

  • Issam El Naqa
    • 1
  • Ruijiang Li
    • 2
  • Martin J. Murphy
    • 3
  1. 1.Department of OncologyMcGill UniversityMontrealCanada
  2. 2.Department of Radiation OncologyStanford University School of MedicineStanfordUSA
  3. 3.Department of Radiation OncologyVirginia Commonwealth UniversityRichmondUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-18305-3
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Medicine
  • Print ISBN 978-3-319-18304-6
  • Online ISBN 978-3-319-18305-3
  • Buy this book on publisher's site