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

TL;DR

This study investigates the use of elastic deformation as a data augmentation technique for OCT images to improve deep-learning models in detecting diabetic macular edema, assessing its clinical validity.

Contribution

It evaluates the effectiveness and limits of elastic deformation for OCT image augmentation in DME detection models.

Findings

Elastic deformation enhances model robustness.

Optimal deformation parameters identified.

Potential for improved diagnostic accuracy.

Abstract

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).