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008			220202s2022 sz | s |||| 0|eng d
020			_a9783030830472
024	7		_a10.1007/978-3-030-83047-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRM847 _b2022 EB
245	0	0	_aMachine and Deep Learning in Oncology, Medical Physics and Radiology _cedited by Issam El Naqa, Martin J. Murphy
250			_aSecond edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVI, 513 páginas) _b168 ilustraciones, 112 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
505	0		_aPart 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.
520			_aThis 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.
988			_aSpringer_Medicine_2022
650		7	_2embne _9139979 _aRadioterapia
650		7	_2embne _9139207 _aOncología
700	1		_aEl Naqa, Issam _eeditor literario
700	1		_aMurphy, Martin J. _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-83047-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2022 _dz _eu _zSI