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Machine and deep learning in oncology, medical physics and radiology

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine and deep learning in oncology, medical physics and radiologyedited by Issam El Naqa, Martin J. Murphy.
其他作者:	El Naqa, Issam.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xvi, 513 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Radiology.
電子資源:	https://doi.org/10.1007/978-3-030-83047-2
ISBN:	9783030830472$q(electronic bk.)

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. - Second edition. - Cham :Springer International Publishing :2022. - xvi, 513 p. :ill. (some col.), digital ;24 cm.

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$q(electronic bk.)

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

188995
Radiology.

LC Class. No.: RC78 / .M36 2022

Dewey Class. No.: 616.0757

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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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