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988 _aSpringer_Medicine_2015
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003 ES-MaUEC
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020 _a9783319183053
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aR859.7.A78
_b2015 EB
245 0 0 _aMachine Learning in Radiation Oncology :
_bTheory and Applications
_cedited by Issam El Naqa, Ruijiang Li, Martin J Murphy
264 1 _aCham, Switzerland
_bSpringer
_c2015
300 _a1 recurso en línea (XIV, 336 p.) 127 il., 67 il. col.
336 _aTexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
338 _aonline resource
_bcr
_2rdacarrier
520 _aThis 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.
650 0 4 _aInteligencia artificial en medicina
_9421371
650 7 _aRadioterapia
_0
_2embne
_9139979
700 1 _aEl Naqa, Issam
_eeditor literario
_995340
_0Local
700 1 _aLi, Ruijiang
_eeditor literario
_995341
_0Local
700 1 _aMurphy, Martin J
_eeditor literario
_995342
_0Local
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-18305-3
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b11/2020
_dz
_eIG
_feng
_ggw
_h0