LEADER 04230nam 22006255 450 001 9910300172503321 005 20200703232747.0 010 $a3-319-18305-2 024 7 $a10.1007/978-3-319-18305-3 035 $a(CKB)3710000000434388 035 $a(EBL)2094811 035 $a(SSID)ssj0001524982 035 $a(PQKBManifestationID)11835283 035 $a(PQKBTitleCode)TC0001524982 035 $a(PQKBWorkID)11495686 035 $a(PQKB)10773293 035 $a(DE-He213)978-3-319-18305-3 035 $a(MiAaPQ)EBC2094811 035 $a(PPN)186396090 035 $a(EXLCZ)993710000000434388 100 $a20150619d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning in Radiation Oncology $eTheory and Applications /$fedited by Issam El Naqa, Ruijiang Li, Martin J. Murphy 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (336 p.) 300 $aDescription based upon print version of record. 311 $a3-319-18304-4 320 $aIncludes bibliographical references and index. 327 $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). 330 $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. 606 $aRadiotherapy 606 $aMedical physics 606 $aRadiation 606 $aRadiotherapy$3https://scigraph.springernature.com/ontologies/product-market-codes/H29056 606 $aMedical and Radiation Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P27060 615 0$aRadiotherapy. 615 0$aMedical physics. 615 0$aRadiation. 615 14$aRadiotherapy. 615 24$aMedical and Radiation Physics. 676 $a610 676 $a610.153 676 $a615842 702 $aEl Naqa$b Issam$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLi$b Ruijiang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMurphy$b Martin J$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910300172503321 996 $aMachine Learning in Radiation Oncology$91867662 997 $aUNINA