  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.
