Facilitating deep learning through preprocessing of optical coherence tomography images.

Journal: BMC ophthalmology
Published Date:

Abstract

BACKGROUND: While deep learning has delivered promising results in the field of ophthalmology, the hurdle to complete a deep learning study is high. In this study, we aim to facilitate small scale model trainings by exploring the role of preprocessing to reduce computational burden and accelerate learning.

Authors

  • Anfei Li
    Department of Ophthalmology, New York Presbyterian Hospital, 1305 York Ave 11th floor, New York, NY, 10021, USA. anl2038@med.cornell.edu.
  • James P Winebrake
    Department of Ophthalmology, New York Presbyterian Hospital, 1305 York Ave 11th floor, New York, NY, 10021, USA.
  • Kyle Kovacs
    Department of Ophthalmology, Weill Cornell Medicine, 1305 York Ave 11th floor, New York, NY, 10021, USA. kyk9011@med.cornell.edu.