Machine learning for tomographic imaging:
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 revolutionise med...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bristol, UK
IOP Publishing
[2020]
|
| Schriftenreihe: | IOP ebooks
IPEM-IOP series in physics and engineering in medicine and biology |
| Schlagworte: | |
| Online-Zugang: | UBR01 UER01 Volltext |
| Zusammenfassung: | 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 revolutionise medical imaging is highly 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 as well. An engaging and accessible style makes this book an ideal introduction for those in applied disciplines, as well as those in more theoretical disciplines 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. Part of Series in Physics and Engineering in Medicine and Biology |
| Beschreibung: | 1 Online-Ressource (xxx, 371 Seiten) Illustrationen, Diagramme |
| ISBN: | 9780750322164 9780750322157 |
Internformat
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| 245 | 1 | 0 | |a Machine learning for tomographic imaging |c Ge Wang (Rensselaer Polytechnic Institute), Yi Zhang (Sichuan University), Xiaojing Ye (Georgia State University), Xuanqin Mou (Xi’an Jiaotong University) |
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Datensatz im Suchindex
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| author | Wang, Ge Zhang, Yi Ye, Xiaojing Mou, Xuanqin |
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| id | DE-604.BV046648286 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:15:40Z |
| indexdate | 2024-07-10T08:50:13Z |
| institution | BVB |
| isbn | 9780750322164 9780750322157 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032059543 |
| oclc_num | 1148151465 |
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| owner_facet | DE-355 DE-BY-UBR DE-29 |
| physical | 1 Online-Ressource (xxx, 371 Seiten) Illustrationen, Diagramme |
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| publishDate | 2020 |
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| publisher | IOP Publishing |
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| series2 | IOP ebooks IPEM-IOP series in physics and engineering in medicine and biology |
| spelling | Wang, Ge Verfasser (DE-588)1207337781 aut Machine learning for tomographic imaging Ge Wang (Rensselaer Polytechnic Institute), Yi Zhang (Sichuan University), Xiaojing Ye (Georgia State University), Xuanqin Mou (Xi’an Jiaotong University) Bristol, UK IOP Publishing [2020] 1 Online-Ressource (xxx, 371 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier IOP ebooks IPEM-IOP series in physics and engineering in medicine and biology 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 revolutionise medical imaging is highly 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 as well. An engaging and accessible style makes this book an ideal introduction for those in applied disciplines, as well as those in more theoretical disciplines 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. Part of Series in Physics and Engineering in Medicine and Biology Medical imaging / bicssc TECHNOLOGY & ENGINEERING / Imaging Systems / bisacsh Tomography Machine learning Artificial intelligence / Medical applications Zhang, Yi Verfasser (DE-588)120733832X aut Ye, Xiaojing Verfasser (DE-588)1207338605 aut Mou, Xuanqin Verfasser (DE-588)1207338885 aut Erscheint auch als Druck-Ausgabe 978-0-7503-2214-0 Erscheint auch als Druck-Ausgabe 978-0-7503-2217-1 https://iopscience.iop.org/book/978-0-7503-2216-4 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Wang, Ge Zhang, Yi Ye, Xiaojing Mou, Xuanqin Machine learning for tomographic imaging Medical imaging / bicssc TECHNOLOGY & ENGINEERING / Imaging Systems / bisacsh Tomography Machine learning Artificial intelligence / Medical applications |
| title | Machine learning for tomographic imaging |
| title_auth | Machine learning for tomographic imaging |
| title_exact_search | Machine learning for tomographic imaging |
| title_exact_search_txtP | Machine learning for tomographic imaging |
| title_full | Machine learning for tomographic imaging Ge Wang (Rensselaer Polytechnic Institute), Yi Zhang (Sichuan University), Xiaojing Ye (Georgia State University), Xuanqin Mou (Xi’an Jiaotong University) |
| title_fullStr | Machine learning for tomographic imaging Ge Wang (Rensselaer Polytechnic Institute), Yi Zhang (Sichuan University), Xiaojing Ye (Georgia State University), Xuanqin Mou (Xi’an Jiaotong University) |
| title_full_unstemmed | Machine learning for tomographic imaging Ge Wang (Rensselaer Polytechnic Institute), Yi Zhang (Sichuan University), Xiaojing Ye (Georgia State University), Xuanqin Mou (Xi’an Jiaotong University) |
| title_short | Machine learning for tomographic imaging |
| title_sort | machine learning for tomographic imaging |
| topic | Medical imaging / bicssc TECHNOLOGY & ENGINEERING / Imaging Systems / bisacsh Tomography Machine learning Artificial intelligence / Medical applications |
| topic_facet | Medical imaging / bicssc TECHNOLOGY & ENGINEERING / Imaging Systems / bisacsh Tomography Machine learning Artificial intelligence / Medical applications |
| url | https://iopscience.iop.org/book/978-0-7503-2216-4 |
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