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2275 results in Pattern Recognition and Machine Learning

Page 11 of 114

  • 8 - Model-Based Deep-Learning Algorithms for Inverse Problems

  • from Part II - Deep-Learning Architecture for Various Imaging Architectures
  • 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
  • Book:
    Deep Learning for Biomedical Image Reconstruction
    Published online:
    15 September 2023
    Print publication:
    12 October 2023, pp 177-199

  • Summary

    This chapter provides a summary of some popular model-based deep learning methods and their extensions. Section 8.1 briefly describes classical model-based methods and their benefit as well as limitations. Section 8.2 describes how deep learning can help in overcoming some limitations of classical model-based methods. Section 8.3 discusses how to incorporate a pre-trained deep network as a regularizer using the plug-and-play approach. Section 8.4 describes end-to-end training using a model-based deep learning framework. This section also discusses some benefits and limitations of end-to-end training. Section 8.5 and 8.6 describe unsupervised model-based deep learning approaches when a clean training dataset is not available. Section 8.6 considers model mismatch issues as well as the joint design of acquisition and reconstruction frameworks.

  • 10 - Deep Learning for Ultrasound Beamforming

  • from Part II - Deep-Learning Architecture for Various Imaging Architectures
  • 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
  • Book:
    Deep Learning for Biomedical Image Reconstruction
    Published online:
    15 September 2023
    Print publication:
    12 October 2023, pp 223-251

  • Summary

    With the present demand for high-quality image reconstruction and signal extraction from less (e.g., unfocused or parallel) transmissions that facilitate fast imaging, and the push towards compact probes, modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, naturally lies at the heart of the ultrasound image formation chain. In this chapter, we discuss why and when deep learning methods can play a compelling role in the digital beamforming pipeline, and then show how these data-driven systems can be leveraged for improved ultrasound image reconstruction.

Deep Learning for Biomedical Image Reconstruction
  • Edited by Jong Chul Ye, Yonina C. Eldar, Michael Unser
  • Published online:
    15 September 2023
    Print publication:
    12 October 2023
  • 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.

Adversarial Learning and Secure AI
  • David J. Miller, Zhen Xiang, George Kesidis
  • Published online:
    07 September 2023
    Print publication:
    31 August 2023
  • Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.

  • 1 - Introduction to Robust Statistics

  • Ilias Diakonikolas, University of Wisconsin-Madison, Daniel M. Kane, University of California, San Diego
  • Book:
    Algorithmic High-Dimensional Robust Statistics
    Published online:
    24 August 2023
    Print publication:
    07 September 2023, pp 1-28
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Page 11 of 114