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  • © 2015

Machine Learning in Radiation Oncology

Theory and Applications

Editors:

  1. Issam El Naqa

    1. Department of Oncology, McGill University, Montreal, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Ruijiang Li

    1. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Martin J. Murphy

    1. Department of Radiation Oncology, Virginia Commonwealth University, Richmond, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Provides a complete overview of the role of machine learning in radiation oncology and medical physics
  • Covers the use of machine learning in quality assurance, computer-aided detection, image-guided radiotherapy, respiratory motion management, and outcome prediction
  • Presents important relevant background information

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Table of contents (18 chapters)

  1. Front Matter

    Pages i-xiv
    PDF

  2. Introduction

    1. Front Matter

      Pages 1-1
      PDF

    2. What Is Machine Learning?

      • Issam El Naqa, Martin J. Murphy
      Pages 3-11

    3. Computational Learning Theory

      • Issam El Naqa
      Pages 13-20

    4. Machine Learning Methodology

      • Sangkyu Lee, Issam El Naqa
      Pages 21-39

    5. Performance Evaluation in Machine Learning

      • Nathalie Japkowicz, Mohak Shah
      Pages 41-56

    6. Informatics in Radiation Oncology

      • Paul Martin Putora, Samuel Peters, Marc Bovet
      Pages 57-70

    7. Application of Machine Learning for Multicenter Learning

      • 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
      PDF

    3. Classification of Malignant and Benign Tumors

      • Juan Wang, Issam El Naqa, Yongyi Yang
      Pages 133-153

  4. Machine Learning for Treatment Planning

    1. Front Matter

      Pages 155-155
      PDF

    2. Image-Guided Radiotherapy with Machine Learning

      • Yaozong Gao, Yanrong Guo, Yinghuan Shi, Shu Liao, Jun Lian, Dinggang Shen
      Pages 157-192

    3. Knowledge-Based Treatment Planning

      • Issam El Naqa
      Pages 193-199

  5. Machine Learning Delivery and Motion Management

    1. Front Matter

      Pages 201-201
      PDF

    3. Image-Based Motion Correction

      • Ruijiang Li
      Pages 225-234

  6. Machine Learning for Quality Assurance

    1. Front Matter

      Pages 235-235
      PDF

    3. Treatment Planning Validation

      • Ruijiang Li, Steve B. Jiang
      Pages 243-252
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About this book

​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.
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Keywords

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Editors and Affiliations

  • Department of Oncology, McGill University, Montreal, Canada

    Issam El Naqa

  • Department of Radiation Oncology, Stanford University School of Medicine, Stanford, USA

    Ruijiang Li

  • Department of Radiation Oncology, Virginia Commonwealth University, Richmond, USA

    Martin J. Murphy

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Bibliographic Information

  • Book Title: Machine Learning in Radiation Oncology

  • Book Subtitle: Theory and Applications

  • Editors: Issam El Naqa, Ruijiang Li, Martin J. Murphy

  • DOI: https://doi.org/10.1007/978-3-319-18305-3

  • Publisher: Springer Cham

  • eBook Packages: Medicine, Medicine (R0)

  • Copyright Information: Springer International Publishing Switzerland 2015

  • Softcover ISBN: 978-3-319-35464-4Published: 12 October 2016

  • eBook ISBN: 978-3-319-18305-3Published: 19 June 2015

  • Edition Number: 1

  • Number of Pages: XIV, 336

  • Number of Illustrations: 60 b/w illustrations, 67 illustrations in colour

  • Topics: Radiotherapy, Medical and Radiation Physics

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Buy it now

eBook USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

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