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Machine and Deep Learning in Oncology, Medical Physics and Radiology

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine and Deep Learning in Oncology, Medical Physics and Radiology/ edited by Issam El Naqa, Martin J. Murphy.
other author:	El Naqa, Issam.
Description:	XVI, 513 p. 168 illus., 112 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Medical radiology. -
Online resource:	https://doi.org/10.1007/978-3-030-83047-2
ISBN:	9783030830472

Machine and Deep Learning in Oncology, Medical Physics and Radiology
Machine and Deep Learning in Oncology, Medical Physics and Radiology[electronic resource] /edited by Issam El Naqa, Martin J. Murphy. - 2nd ed. 2022. - XVI, 513 p. 168 illus., 112 illus. in color.online resource.

Part I. Introduction -- 1. What are Machine and Deep Learning? -- 2. Computational Learning Basics -- 3. Overview of Conventional Machine Learning Methods -- 4. Overview of Deep Machine Learning Methods -- 5. Quantum Computing for Machine Learning -- 6. Performance Evaluation -- 7. Software Tools for Machine and Deep learning -- 8. Data sharing, protection and bioethics -- Part II. Machine Learning for Medical Image Analysis -- 9. Detection of Cancer Lesions from Imaging -- 10. 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.

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. .

ISBN: 9783030830472

Standard No.: 10.1007/978-3-030-83047-2doiSubjects--Topical Terms:

1064555
Medical radiology.

LC Class. No.: R895-920

Dewey Class. No.: 616.0757

Machine and Deep Learning in Oncology, Medical Physics and Radiology
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520 $a 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. .
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