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Background. Digital pathology has aroused widespread interest in modernpathology. The key of digitalization is to scan the whole slide image (WSI) athigh magnification. The lager the magnification is, the richer details WSI willprovide, but the scanning time is longer and the file size of obtained islarger. Methods. We design a strategy to scan slides with low resolution (5X)and a super-resolution method is proposed to restore the image details when indiagnosis. The method is based on a multi-scale generative adversarial network,which sequentially generates three high-resolution images such as 10X, 20X and40X. Results. The peak-signal-to-noise-ratio of 10X to 40X generated images are24.16, 22.27 and 20.44, and the structural-similarity-index are 0.845, 0.680and 0.512, which are better than other super-resolution networks. Visualscoring average and standard deviation from three pathologists is 3.63plus-minus 0.52, 3.70 plus-minus 0.57 and 3.74 plus-minus 0.56 and the p valueof analysis of variance is 0.37, indicating that generated images includesufficient information for diagnosis. The average value of Kappa test is 0.99,meaning the diagnosis of generated images is highly consistent with that of thereal images. Conclusion. This proposed method can generate high-quality 10X,20X, 40X images from 5X images at the same time, in which the time and storagecosts of digitalization can be effectively reduced up to 1/64 of the previouscosts. The proposed method provides a better alternative for low-cost storage,faster image share of digital pathology. Keywords. Digital pathology;Super-resolution; Low resolution scanning; Low cost

Multi-scale super-resolution generation of low-resolution scanned pathological images