Learning to Discover Explainable Clinical Features With Minimum Supervision.

Journal: Translational vision science & technology
Published Date: Jan 3, 2022

Abstract

PURPOSE: To compare supervised transfer learning to semisupervised learning for their ability to learn in-depth knowledge with limited data in the optical coherence tomography (OCT) domain.

Authors

  • Lutfiah Al Turk
    Department of Statistics, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.
  • Darina Georgieva
    Department of Computer Science, University of Surrey, Guildford, Surrey, UK.
  • Hassan Alsawadi
    Department of Electrical and Computer Engineering, King Abdulaziz, University, Jeddah, Kingdom of Saudi Arabia.
  • Su Wang
    Department of Computer Science, University of Surrey, Guildford, Surrey, UK.
  • Paul Krause
    University of Surrey, Guildford, UK.
  • Hend Alsawadi
    Faculty of Medicine, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.
  • Abdulrahman Zaid Alshamrani
    Ophthalmology Department, Faculty of Medicine, University of Jeddah, Kingdom of Saudi Arabia.
  • George M Saleh
    NIHR Biomedical Research Centre at Moorfields Eye Hospital and the UCL Institute of Ophthalmology, UK.
  • Hongying Lilian Tang
    Department of Computer Science, University of Surrey, Guildford, Surrey, UK.

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