# Semantic denoising autoencoders for retinal optical coherence tomography

## TL;DR

This paper introduces a novel denoising autoencoder for retinal OCT images that preserves disease features and improves image quality and classification accuracy over existing methods.

## Contribution

It combines a deep convolutional autoencoder with a pre-trained ResNet classifier as a regularizer, effectively denoising images while maintaining diagnostic details.

## Key findings

- Higher PSNR of 31.2 dB compared to 29.4 dB.
- Increased classification accuracy to 85.0% from 70.3%.
- Preserves disease details without blurring.

## Abstract

Noise in speckle-prone optical coherence tomography tends to obfuscate important details necessary for medical diagnosis. In this paper, a denoising approach that preserves disease characteristics on retinal optical coherence tomography images in ophthalmology is presented. By combining a deep convolutional autoencoder with a priorly trained ResNet image classifier as regularizer, the perceptibility of delicate details is encouraged and only information-less background noise is filtered out. With our approach, higher peak signal-to-noise ratios with $ \mathrm{PSNR} = 31.2\,\mathrm{dB} $ and higher classification accuracy of $\mathrm{ACC} = 85.0\,\%$ can be achieved for denoised images compared to state-of-the-art denoising with $ \mathrm{PSNR} = 29.4\,\mathrm{dB} $ or $\mathrm{ACC} = 70.3\,\%$, depending on the method. It is shown that regularized autoencoders are capable of denoising retinal OCT images without blurring details of diseases.