Automatic recognition of tumour-infiltrating lymphocytes in pathological biopsy images of the gastric mucosa

Figure 2 Schematic workflow and multiscale convolutional neural network architecture for tumour-infiltrating lymphocyte recognition in gastric mucosa. The pipeline consists of four primary stages: (1) Dataset preparation: The study cohort was divided into a training set and an independent test set; (2) Image preprocessing: Original hematoxylin and eosin images were decomposed into 2-channel (H and E) components via color deconvolution to resolve channel dimension mismatch and enhance staining robustness; (3) Model core: A multiscale convolutional neural network architecture featuring parallel branches and attention mechanisms was employed to extract multi-resolution features; and (4) Output and visualization: The model generates attention heatmaps that precisely highlight tumour-infiltrating lymphocyte nuclear regions (red) while effectively suppressing interference from background structures such as goblet cells. H/E: Hematoxylin and eosin.