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Elastic deformation of optical coherence tomography images of diabetic macular edema for deep-learning models training: how far to go?

arXiv 2107.03651 ↗First posted 8 Jul 2021eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

To explore the clinical validity of elastic deformation of optical coherence tomography (OCT) images for data augmentation in the development of deep-learning model for detection of diabetic macular edema (DME).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 23 references that are in the map. First posted on arXiv 8 Jul 2021.

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References · 3

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Imagescited by 6
  3. 2017Simultaneous Detection and Quantification of Retinal Fluid with Deep Learningcited by 3

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