Multimodality Imaging, Volume 1 : Deep learning applications
Language: English
Published by IOP Publishing Dez 2022, 2022
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
US$ 55.43
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Add to basketItem description from seller
Neuware - This research and reference text explores the finer details of Deep Learning models. It provides a brief outline on popular models including convolution neural networks (CNN), deep belief networks (DBN), autoencoders, residual neural networks (Res Nets). The text discusses some of the Deep Learning-based applications in gene identification. Sections in the book explore the foundation and necessity of deep learning in radiology, the application of deep learning in the area of cardiovascular imaging and deep learning applications in the area of fatty liver disease characterization and COVID19, respectively.This reference text is highly relevant for medical professionals and researchers in the area of AI in medical imaging.Key Features: - Discusses various diseases related to lung, heart, peripheral arterial imaging, as well as gene expression characterization and classification- Explores imaging applications, their complexities and the Deep Learning models employed to resolve them in detail- Provides state-of-the-art contributions while addressing doubts in multimodal research- Details the future of deep learning and big data in medical imaging.…
Seller Inventory # 9780750322454
- Title
- Multimodality Imaging, Volume 1 : Deep learning applications
- Author
- Mainak Biswas
- Publisher
- IOP Publishing Dez 2022
- Publication year
- 2022
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0750322454
- ISBN 13
- 9780750322454
- Item weight
- 619 grams
- Dimensions
- 254x178x19 mm
This research and reference text explores the finer details of Deep Learning models. It provides a brief outline on popular models including convolution neural networks (CNN), deep belief networks (DBN), autoencoders, residual neural networks (Res Nets). The text discusses some of the Deep Learning-based applications in gene identification. Sections in the book explore the foundation and necessity of deep learning in radiology, the application of deep learning in the area of cardiovascular imaging and deep learning applications in the area of fatty liver disease characterization and COVID19, respectively.
This reference text is highly relevant for medical professionals and researchers in the area of AI in medical imaging.
Key Features:
- Discusses various diseases related to lung, heart, peripheral arterial imaging, as well as gene expression characterization and classification
- Explores imaging applications, their complexities and the Deep Learning models employed to resolve them in detail
- Provides state-of-the-art contributions while addressing doubts in multimodal research
- Details the future of deep learning and big data in medical imaging
"Synopsis" may belong to another edition of this title.
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