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Deep Learning for Biomedical Image Reconstruction, Hardback Book

Deep Learning for Biomedical Image Reconstruction Hardback

Edited by Jong Chul (Korea Advanced Institute of Science and Technology (KAIST)) Ye, Yonina C. (Weizmann Institute of Science, Israel) Eldar, Michael (Ecole Polytechnique Federale de Lausanne) Unser

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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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