Deep learning in macroscopic diffuse optical imaging.

Journal: Journal of biomedical optics
PMID: 35218169

Abstract

SIGNIFICANCE: Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in computational modeling and has demonstrated utility in numerous scientific domains and various forms of data analysis.

Authors

  • Jason T Smith
    Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180; smithj28@rpi.edu intesx@rpi.edu.
  • Marien Ochoa
    Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York, 12180.
  • Denzel Faulkner
    Rensselaer Polytechnic Institute, Department of Biomedical Engineering, Troy, New York, United States.
  • Grant Haskins
    Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
  • Xavier Intes
    Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY.

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