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020			_a9783030830472
024	7		_a10.1007/978-3-030-83047-2 _2doi
040			_aTR-AnTOB _beng _erda _cTR-AnTOB
060			_aWN 250
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245	1	0	_aMachine and Deep Learning in Oncology, Medical Physics and Radiology _h[electronic resource] / _cedited by Issam El Naqa, Martin J. Murphy.
250			_a2nd ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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. .
650		0	_aMedical radiology.
650		0	_aOncology.
650		0	_aMachine learning.
650		0	_aMedical physics.
650		0	_aRadiology.
650		0	_aBiophysics.
650	1	4	_aRadiation Oncology.
650	2	4	_aMachine Learning.
650	2	4	_aOncology.
650	2	4	_aMedical Physics.
650	2	4	_aRadiology.
650	2	4	_aBiophysics.
653		0	_aRadiotherapy
653		0	_aArtificial Intelligence
700	1		_aEl Naqa, Issam. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aMurphy, Martin J. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
856	4	0	_uhttps://doi.org/10.1007/978-3-030-83047-2 _3Springer eBooks _zOnline access link to the resource
942			_2NLM _cEBK
041			_aeng