Ensemble Deep Learning for Diabetic Retinopathy Detection Using Optical Coherence Tomography Angiography.

Journal: Translational vision science & technology
Published Date: Apr 13, 2020

Abstract

PURPOSE: To evaluate the role of ensemble learning techniques with deep learning in classifying diabetic retinopathy (DR) in optical coherence tomography angiography (OCTA) images and their corresponding co-registered structural images.

Authors

  • Morgan Heisler
    Simon Fraser University, School of Engineering Science, Burnaby V5A 1S6, Canada.
  • Sonja Karst
    Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
  • Julian Lo
    School of Engineering Science, Simon Fraser University, Burnaby, Canada.
  • Zaid Mammo
    Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
  • Timothy Yu
    School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada.
  • Simon Warner
    Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
  • David Maberley
    Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
  • Mirza Faisal Beg
    School of Engineering Science, Simon Fraser University, Burnaby, Canada.
  • Eduardo V Navajas
    Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
  • Marinko V Sarunic
    School of Engineering Science, Simon Fraser University, Burnaby, Canada.

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