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6778 results in Communications and signal processing

Page 22 of 339

  • 1 - Introduction

  • Jing Li, University of Houston, Dusit Niyato, Nanyang Technological University, Singapore, Zhu Han, University of Houston
  • Book:
    Cryptoeconomics
    Published online:
    06 January 2024
    Print publication:
    16 November 2023, pp 1-20
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  • 1 - Formalizing Deep Neural Networks

  • from Part I - Theory of Deep Learning for Image Reconstruction
  • 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 3-12

  • Summary

    We provide a short, self-contained introduction to deep neural networks that is aimed at mathematically inclined readers. We promote the use of a vect--matrix formalism that is well suited to the compositional structure of these networks and that facilitates the derivation/description of the backpropagation algorithm. We present a detailed analysis of supervised learning for the two most common scenarios, (i) multivariate regression and (ii) classification, which rely on the minimization of least squares and cross-entropy criteria, respectively.

  • 2 - Geometry of Deep Learning

  • from Part I - Theory of Deep Learning for Image Reconstruction
  • 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 13-27

  • Summary

    Since the groundbreaking performance improvement by AlexNet at the ImageNet challenge, deep learning has provided significant gains over classical approaches in various fields of data science including imaging reconstruction. The availability of large-scale training datasets and advances in neural network research have resulted in the unprecedented success of deep learning in various applications. Nonetheless, the success of deep learning appears very mysterious. The basic building blocks of deep neural networks are convolution, pooling, and nonlinearity, which are primitive tools of mathematics. Interestingly, the cascaded connection of these primitive tools results in superior performance over traditional approaches. To understand this mystery, one can go back to the basic ideas of the classical approaches to understand the similarities and differences from modern deep-neural-network methods. In this chapter, we explain the limitations of the classical machine learning approaches, and provide a review of mathematical foundations to understand why deep neural networks have successfully overcome their limitations.

  • 9 - k-Space Deep Learning for MR Reconstruction and Artifact Removal

  • 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 200-222

  • Summary

    Inspired by the success of deep learning in computer vision tasks, deep learning approaches for various MRI problems have been extensively studied in recent years. Early deep learning studies for MRI reconstruction and enhancement were mostly based on image-domain learning. However, because the MR signal is acquired in the k-space domain, researchers have demonstrated that deep neural networks can be directly designed in k-space to utilize the physics of MR acquisition. In this chapter, the recent trend of k-space deep learning for MRI reconstruction and artifact removal are reviewed. First, scan-specific k-space learning, which is inspired by parallel MRI, is covered. Then we provide an overview of data-driven k-space learning. Subsequently, unsupervised learning for MRI reconstruction and motion artifact removal are discussed.

Page 22 of 339