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Histopathology plays a pivotal role in medical diagnostics. In contrast topreparing permanent sections for histopathology, a time-consuming process,preparing frozen sections is significantly faster and can be performed duringsurgery, where the sample scanning time should be optimized. Super-resolutiontechniques allow imaging the sample in lower magnification and sparing scanningtime. In this paper, we present a new approach to super resolution forhistopathological frozen sections, with focus on achieving better distortionmeasures, rather than pursuing photorealistic images that may compromisecritical diagnostic information. Our deep-learning architecture focuses onlearning the error between interpolated images and real images, thereby itgenerates high-resolution images while preserving critical image details,reducing the risk of diagnostic misinterpretation. This is done by leveragingthe loss functions in the frequency domain, assigning higher weights to thereconstruction of complex, high-frequency components. In comparison to existingmethods, we obtained significant improvements in terms of Structural SimilarityIndex (SSIM) and Peak Signal-to-Noise Ratio (PSNR), as well as indicateddetails that lost in the low-resolution frozen-section images, affecting thepathologist's clinical decisions. Our approach has a great potential inproviding more-rapid frozen-section imaging, with less scanning, whilepreserving the high resolution in the imaged sample.

Super resolution of histopathological frozen sections via deep learning preserving tissue structure