Impact of blurs on machine-learning aided digital pathology image analysis

Figure 1 The schema of this study. A: Hematoxylin and eosin stained slides of gastric biopsy specimens were scanned twice by the same slide scanner, then the paired digital images were created; B: The paired images were independently analyzed and classified by our machine-learning model. If concordant classification results were obtained, the case is “Non-flipped”; if discordant classification results were obtained, the case is “Flipped.” Then, color and blur differences were compared between the “Non-flipped” and “Flipped” groups.