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.

Figure formula 1 

Figure 2 Differences in the blur index between the “Non-flipped” and “Flipped” groups.

Figure 3 Typical examples of differences in the blurring level. A: Whole-slide image at the first scan; B: Whole-slide image at the second scan; C: The blurring level at the first scan (blur index = 115); D: The blurring level at the second scan (blur index = 78); E: A heat map representation of the blurring level at the first scan; F: A heat map representation of the blurring level at the second scan.