Machine Learning in Radiation Oncology: Theory and Applications
This 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...
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| Ngôn ngữ: | English |
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Springer
2016
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| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/59962 |
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oai:scholar.dlu.edu.vn:DLU123456789-599622023-11-11T06:58:04Z Machine Learning in Radiation Oncology: Theory and Applications El Naqa, Issam Li, Ruijiang Murphy, Martin J Data processing Radiotherapy Machine learning Medical applications Artificial intelligence This 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. 2016-03-31T03:19:24Z 2016-03-31T03:19:24Z 2015 Book 978-3-319-18305-3 978-3-319-18304-6 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/59962 en application/pdf Springer |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
Data processing Radiotherapy Machine learning Medical applications Artificial intelligence |
| spellingShingle |
Data processing Radiotherapy Machine learning Medical applications Artificial intelligence El Naqa, Issam Li, Ruijiang Murphy, Martin J Machine Learning in Radiation Oncology: Theory and Applications |
| description |
This 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. |
| format |
Book |
| author |
El Naqa, Issam Li, Ruijiang Murphy, Martin J |
| author_facet |
El Naqa, Issam Li, Ruijiang Murphy, Martin J |
| author_sort |
El Naqa, Issam |
| title |
Machine Learning in Radiation Oncology:
Theory and Applications |
| title_short |
Machine Learning in Radiation Oncology:
Theory and Applications |
| title_full |
Machine Learning in Radiation Oncology:
Theory and Applications |
| title_fullStr |
Machine Learning in Radiation Oncology:
Theory and Applications |
| title_full_unstemmed |
Machine Learning in Radiation Oncology:
Theory and Applications |
| title_sort |
machine learning in radiation oncology:
theory and applications |
| publisher |
Springer |
| publishDate |
2016 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/59962 |
| _version_ |
1819811940136910848 |