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

Figure 3 Performance evaluation of the gastric tumour-infiltrating lymphocyte-convolutional neural network model for tumour-infiltrating lymphocyte recognition. Model performance was evaluated on 31104 test set patches. A: The receiver operating characteristic curve shows excellent discriminative ability for tumour-infiltrating lymphocyte (TILs)/non-TILs; B: The confusion matrix reveals 100.0% specificity (no false positives) and 98.7% sensitivity (1.3% miss rate); C: The scatter plot shows a strong positive correlation (Pearson r = 0.93, P < 0.001) between the gastric artificial intelligence-based TIL and the pathologist-manual TIL density, confirming quantitative consistency; D: The bar chart shows that the model outperforms the manual evaluation in terms of accuracy (99.2% vs 86.3%) and kappa coefficient (0.98 vs 0.72, P < 0.001), indicating that the interobserver variability is reduced. ROC: Receiver operating characteristic; AUC: Area under the curve; TIL: Tumour-infiltrating lymphocyte; CNN: Convolutional neural network; G-AI-TIL: Gastric artificial intelligence-based tumor-infiltrating lymphocytes.