Ipem-iop series in physics and engineering in medicine and biology - 9780750322171 - machine learning for tomographic imaging von wang, professor ge; zhang, professor yi; ye, professor xiaojing (4 Ergebnisse)

Machine Learning for Tomographic Imaging
Wang, Professor Ge; Zhang, Professor Yi; Ye, Professor Xiaojing
Sprache: Englisch
Verlag: IOP Publishing Ltd, 2019
Serie: Buch 16 von 36 - IPEM-IOP Series in Physics and Engineering in Medicine and Biology
- Softcover
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Machine Learning for Tomographic Imaging
Wang, Professor Ge; Zhang, Professor Yi; Ye, Professor Xiaojing
Sprache: Englisch
Verlag: IOP Publishing Ltd, 2019
Serie: Buch 16 von 36 - IPEM-IOP Series in Physics and Engineering in Medicine and Biology
- Softcover
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Machine Learning for Tomographic Imaging
Wang, Professor Ge; Zhang, Professor Yi; Ye, Professor Xiaojing
Sprache: Englisch
Verlag: Institute of Physics Publishing, 2019
Serie: Buch 16 von 36 - IPEM-IOP Series in Physics and Engineering in Medicine and Biology
- Softcover
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Sprache: Englisch
Verlag: IOP Publishing Dez 2019, 2019
Serie: Buch 16 von 36 - IPEM-IOP Series in Physics and Engineering in Medicine and Biology
- Softcover
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Taschenbuch. Zustand: Neu. Neuware - The area of machine learning, especially deep learning, has exploded in recent years, producing advances in everything from speech recognition and gaming to drug discovery. Tomographic imaging is another major area that is being transformed by machine learning, and its potential to revolution…ise medical imaging is significant.Written by active researchers in the field, Machine Learning for Tomographic Imaging presents a unified overview of deep-learning-based tomographic imaging. Key concepts, including classic reconstruction ideas and human vision inspired insights, are introduced as a foundation for a thorough examination of artificial neural networks and deep tomographic reconstruction. X-ray CT and MRI reconstruction methods are covered in detail, and other medical imaging applications are discussed.An engaging and accessible style makes this book an ideal introduction for those in applied disciplines, as well as those in more theoretical fields who wish to learn about application contexts. Hands-on projects are also suggested, and links to open source software, working datasets, and network models are included.