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

arXiv: https://arxiv.org/abs/2107.03651 · HTML: https://mlsanity.com/paper/2107.03651/

## 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.

## Builds on (most cited first, 3 of 3)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images](https://mlsanity.com/paper/1803.00232/) (2018)
- [Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning](https://mlsanity.com/paper/1708.05464/) (2017)

## 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).