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  • Edited by Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST), Yonina C. Eldar, Weizmann Institute of Science, Israel, Michael Unser, École Polytechnique Fédérale de Lausanne
Cambridge University Press
Online publication date:
September 2023
Print publication year:
Online ISBN:

Book description

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.

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  • 1 - Formalizing Deep Neural Networks
    pp 3-12


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