Development of pathological reconstructed high‐resolution images using artificial intelligence based on whole slide image

Abstract

Pathology plays a very important role in cancer diagnosis. The rapid development of digital pathology (DP) based on whole slide image (WSI) has led to many improvements in computer-assisted diagnosis by artificial intelligence. The common digitization strategy is to scan the pathology slice with 20× or 40× objective, and the 40× objective requires excessive storage space and transmission time, which are significant negative factors in the popularization of DP. In this article, we present a novel reconstructed high-resolution (HR) process based on deep learning to switch 20 × WSI to 40 × without the loss of whole and local features. Furthermore, we collected the WSI data of 100 uterine leiomyosarcomas and 100 adult granulosa cell tumors to test our reconstructed HR process. We tested the reconstructed HR WSI by the peak signal-to-noise ratio, structural similarity, and the blind/reject image spatial quality evaluator, which were 42.03, 0.99, and 49.22, respectively. Subsequently, we confirmed the consistency between the actual and our reconstructed HR images. The testing results indicate that the reconstructed HR imaging is a reliable method for the digital slides of a variety

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