000727812 000__ 03825cam\a2200469Ii\4500
000727812 001__ 727812
000727812 005__ 20230306140937.0
000727812 006__ m\\\\\o\\d\\\\\\\\
000727812 007__ cr\cn\nnnunnun
000727812 008__ 150623s2015\\\\sz\a\\\\ob\\\\001\0\eng\d
000727812 020__ $$a9783319183053$$qelectronic book
000727812 020__ $$a3319183052$$qelectronic book
000727812 020__ $$z9783319183046
000727812 0247_ $$a10.1007/978-3-319-18305-3$$2doi
000727812 035__ $$aSP(OCoLC)ocn911386369
000727812 035__ $$aSP(OCoLC)911386369
000727812 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dGW5XE$$dN$T$$dIDEBK$$dYDXCP$$dEBLCP$$dUPM
000727812 049__ $$aISEA
000727812 050_4 $$aR859.7.A78
000727812 08204 $$a610.28$$223
000727812 24500 $$aMachine learning in radiation oncology$$h[electronic resource] :$$btheory and applications /$$cIssam El Naqa, Ruijiang Li, Martin J. Murphy, editors.
000727812 264_1 $$aCham :$$bSpringer,$$c2015.
000727812 300__ $$a1 online resource (xiv, 336 pages) :$$billustrations.
000727812 336__ $$atext$$btxt$$2rdacontent
000727812 337__ $$acomputer$$bc$$2rdamedia
000727812 338__ $$aonline resource$$bcr$$2rdacarrier
000727812 504__ $$aIncludes bibliographical references and index.
000727812 5050_ $$aIntroduction: What is Machine Learning -- Computational Learning Theory -- Overview of Supervised Learning Methods -- Overview of Unsupervised Learning Methods -- Performance Evaluation -- Variety of Applications in Radiation Oncology -- Machine Learning for Quality Assurance: Quality Assurance as a Learning Problem -- Detection of Radiotherapy Errors Using Unsupervised Learning -- Prediction of Radiotherapy Errors Using Supervised Learning -- Machine Learning for Computer-Aided Detection: Detection of Cancer Lesions from Imaging -- Classification of Malignant and Benign Tumours -- Machine Learning for Treatment Planning and Delivery -- Image-guided Radiotherapy with Machine Learning: IMRT Optimization Using Machine Learning -- Treatment Assessment Tools -- Machine Learning for Motion Management: Prediction of Respiratory Motion -- Motion-Correction Using Learning Methods -- Machine Learning Application in 4D-CT -- Machine Learning Application in Dynamic Delivery -- Machine Learning for Outcomes Modeling: Bioinformatics of Treatment Response -- Modelling of Norma Tissue Complication Probabilities (NTCP) -- Modelling of Tumour Control Probability (TCP).
000727812 506__ $$aAccess limited to authorized users.
000727812 520__ $$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.
000727812 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 23, 2015).
000727812 650_0 $$aArtificial intelligence$$xMedical applications.
000727812 650_0 $$aMachine learning.
000727812 650_0 $$aRadiotherapy$$xData processing.
000727812 7001_ $$aNaqa, Issam El,$$eeditor.
000727812 7001_ $$aLi, Ruijiang,$$eeditor.
000727812 7001_ $$aMurphy, Martin J.,$$eeditor.
000727812 77608 $$iPrint version:$$z9783319183046
000727812 85280 $$bebk$$hSpringerLink
000727812 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-18305-3$$zOnline Access$$91397441.1
000727812 909CO $$ooai:library.usi.edu:727812$$pGLOBAL_SET
000727812 980__ $$aEBOOK
000727812 980__ $$aBIB
000727812 982__ $$aEbook
000727812 983__ $$aOnline
000727812 994__ $$a92$$bISE