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Elastic Deformation of Optical Coherence Tomography Images of Diabetic Macular Edema for Deep-Learning Models Training: How Far to Go?

Daniel Bar-DavidLaura Bar-DavidYinon ShapiraRina LeibuDalia DoriAseel GebaraRonit SchneorAnath FischerShiri Soudry
Published in: IEEE journal of translational engineering in health and medicine (2023)
Deformation of low-medium intensity ([Formula: see text] = 1-9) may be applied without compromising OCT image representativeness in DME. Clinical and Translational Impact Statement-Elastic deformation may efficiently augment the size, robustness, and diversity of training datasets without altering their clinical value, enhancing the development of high-accuracy algorithms for automated interpretation of OCT images.
Keyphrases
  • deep learning
  • optical coherence tomography
  • convolutional neural network
  • artificial intelligence
  • machine learning
  • diabetic retinopathy
  • optic nerve
  • high intensity
  • preterm birth