Items related to Multimodality Imaging: Deep Learning Applications (IOP...
Synopsis
This book provides technical details of the application of deep learning and machine learning in medical imaging for diagnosis brain, cardiovascular, liver, lung diseases. The text also explores the technical and ethical aspects of deep learning and machine learning applications in medical imaging.
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Multimodality Imaging, Volume 1
Published by
Institute of Physics Publishing, 2022
ISBN 10: 075032242X
ISBN 13: 9780750322423
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # CX-9780750322423
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Multimodality Imaging, Volume 1
Published by
Institute of Physics Publishing, 2022
ISBN 10: 075032242X
ISBN 13: 9780750322423
New
Hardcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
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HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # CX-9780750322423
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Multimodality Imaging : Deep Learning Applications
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Multimodality Imaging: Deep Learning Applications (Iop eBooks)
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Condition: New. In English. Seller Inventory # ria9780750322423_new
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Multimodality Imaging : Deep Learning Applications
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Multimodality Imaging : Deep Learning Applications
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Multimodality Imaging : Deep Learning Applications
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Multimodality Imaging: Deep Learning Applications (Iop eBooks)
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Multimodality Imaging
Seller: moluna, Greven, Germany
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Condition: New. KlappentextThis 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, resid. Seller Inventory # 598727936
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Multimodality Imaging, Volume 1 : Deep learning applications
Published by
IOP Publishing Ltd Dez 2022, 2022
ISBN 10: 075032242X
ISBN 13: 9780750322423
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Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Buch. Condition: Neu. 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 # 9780750322423