The Workshop on Deep Learning for Biomedical Image Reconstruction will be held as part of the 2020 IEEE International Symposium on Biomedical Imaging (ISBI). Machine learning has recently received a large amount of interest for the reconstruction of biomedical and pre-clinical imaging datasets. This workshop focuses on the recent developments and challenges in machine learning for image reconstruction, and its focus is on original work aimed to develop new state-of-the-art techniques and their biomedical imaging applications.

CODE: 9gabt
 

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The workshop scope covers the machine learning techniques that are used for image acquisition and reconstruction. Specific topics are as follows:

[1] Scope

  • Physics-driven neural networks
  • Sensor-domain methods
  • Neural network optimization and design
  • Generative approaches
  • Non-neural network approaches
  • Denoising, super-resolution and image synthesis methods
  • Applications in computed tomography, magnetic resonance imaging, ultrasound imaging and nuclear imaging
  • Applications in optical imaging and microscopy
  • Multimodal reconstructions or data fusion
  • Clinical evaluation
  • Challenges for clinical translation

[2] Organizers

  1. Mehmet Akcakaya
    Department of Electrical and Computer Engineering
    University of Minnesota
    Minneapolis, Minnesota
  2. Ge Wang 
    Department of Biomedical Engineering
    Rensselaer Polytechnic Institute
    Troy, New York
  3. Jong Chul Ye
    Department of Bio and Brain Engineering
    Korea Advanced Institute of Science & Technology (KAIST)
    Daejeon 305-701, Korea

[3] Program

  • Morning session:  Theory and Algorithms
  • Afternoon session: Clinical/Industrial Applications

 

Invited Speakers

Yoram Bresler

University of Illinois at Urbana-Champaign

Tim Leiner

Utrecht University Medical Center

Zhou Yu

Canon Medical Research

Bruno De Man

GE Global Research

Mariya Doneva

Philips Research

Mariappan Nadar

Siemens Healthineers