  Background. Digital pathology has aroused widespread interest in modern
pathology. The key of digitalization is to scan the whole slide image (WSI) at
high magnification. The lager the magnification is, the richer details WSI will
provide, but the scanning time is longer and the file size of obtained is
larger. 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 in
diagnosis. The method is based on a multi-scale generative adversarial network,
which sequentially generates three high-resolution images such as 10X, 20X and
40X. Results. The peak-signal-to-noise-ratio of 10X to 40X generated images are
24.16, 22.27 and 20.44, and the structural-similarity-index are 0.845, 0.680
and 0.512, which are better than other super-resolution networks. Visual
scoring average and standard deviation from three pathologists is 3.63
plus-minus 0.52, 3.70 plus-minus 0.57 and 3.74 plus-minus 0.56 and the p value
of analysis of variance is 0.37, indicating that generated images include
sufficient information for diagnosis. The average value of Kappa test is 0.99,
meaning the diagnosis of generated images is highly consistent with that of the
real 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 storage
costs of digitalization can be effectively reduced up to 1/64 of the previous
costs. 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
