Semantic denoising autoencoders for retinal optical coherence tomography
This addresses noise removal in ophthalmology imaging to improve diagnosis, but it is incremental as it combines existing autoencoder and classifier methods.
The paper tackled noise in retinal optical coherence tomography images that obscures details for medical diagnosis, achieving a PSNR of 31.2 dB and classification accuracy of 85.0%, outperforming state-of-the-art methods.
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.