Items related to Deep Learning for Biomedical Image Reconstruction
- PublisherCambridge University Press
- Publication date2023
- ISBN 10 1316517519
- ISBN 13 9781316517512
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages400
- EditorYe Jong Chul, Eldar Yonina C., Unser Michael
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Deep Learning for Biomedical Image Reconstruction
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Cambridge University Press, 2023
ISBN 10: 1316517519
ISBN 13: 9781316517512
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Deep Learning for Biomedical Image Reconstruction
Published by
Cambridge University Press, 2023
ISBN 10: 1316517519
ISBN 13: 9781316517512
Used
Hardcover
Seller: Goodbooks Company, Springdale, AR, U.S.A.
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Condition: good. Has a sturdy binding with some shelf wear. May have some markings or highlighting. Used copies may not include access codes or Cd's. Slight bending may be present. Seller Inventory # GBV.1316517519.G
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Deep Learning for Biomedical Image Reconstruction
Published by
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ISBN 10: 1316517519
ISBN 13: 9781316517512
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Deep Learning for Biomedical Image Reconstruction
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ISBN 10: 1316517519
ISBN 13: 9781316517512
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Deep Learning for Biomedical Image Reconstruction
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ISBN 10: 1316517519
ISBN 13: 9781316517512
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Deep Learning for Biomedical Image Reconstruction
Published by
Cambridge University Press, 2023
ISBN 10: 1316517519
ISBN 13: 9781316517512
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Hardcover
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Deep Learning for Biomedical Image Reconstruction
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ISBN 10: 1316517519
ISBN 13: 9781316517512
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Deep Learning for Biomedical Image Reconstruction
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ISBN 10: 1316517519
ISBN 13: 9781316517512
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Deep Learning for Biomedical Image Reconstruction
Published by
Cambridge University Press, 2023
ISBN 10: 1316517519
ISBN 13: 9781316517512
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Deep Learning for Biomedical Image Reconstruction (Hardcover)
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Cambridge University Press, Cambridge, 2023
ISBN 10: 1316517519
ISBN 13: 9781316517512
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Hardcover. Condition: new. Hardcover. Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. The background theory of deep learning is introduced step-by-step, and by incorporating modeling fundamentals this book explains how to implement deep learning in a variety of modalities, including X-ray, CT, MRI and others. Real-world examples demonstrate an interdisciplinary approach to medical image reconstruction processes, featuring numerous imaging applications. Recent clinical studies and innovative research activity in generative models and mathematical theory will inspire the reader towards new frontiers. This book is ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics. Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. Including interdisciplinary examples and a step-by-step background of deep learning, this book provides insight into the future of biomedical image reconstruction with clinical studies and mathematical theory. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781316517512
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