Fig. 1. White light microscopy images of H&E stained thin sections of three different human gastric endoscopy biopsies: (a) healthy control (c) chronic gastritis; (e) gastric cancer. The insets (b), (d) and (f) show the enlarged black box zones of the images (a), (b) and (e), respectively. The scale bar is 200 μm in (a), (c) and (e) and 100 μm in (b), (d) and (f).

Fig. 2. Mueller microscopy images of unstained thin sections of three different human gastric endoscopy biopsies. Normalized transmitted intensity – (a) healthy control, (c) chronic gastritis; (e) gastric cancer. Map of scalar retardance – (b) healthy control, (d) chronic gastritis; (f) gastric cancer. Field of view is about 250 μm in diameter.

Fig. 3. Selection of the region of interest (red box – 400 × 300 pixels, left panel) and corresponding training dataset (size of 4 × 400 × 300) – the images of transmitted intensity, linear retardance, linear dichroism, and circular depolarization of healthy control gastric tissue.

Fig. 4. The main steps of building the regression model using training set of the intensity and polarimetric images of healthy control gastric tissue section.

Fig. 5. Predicted images (400 × 300 pixels) of gastric tissue sections: (a) healthy, (b) chronic gastritis, (c) gastric cancer), (d–f) histograms of the corresponding predicted images; (g–i) insets with the enlarged view of the second Gaussian peak.