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

Table 1 Performance indicators of the gastric-tumour-infiltrating lymphocytes-convolutional neural network model in the training set and test set (%)

Dataset	Accuracy (95%CI)	Specificity (95%CI)	Sensitivity (95%CI)	Cohen’s Kappa (95%CI)	F1 score (95%CI)	Compared with manual assessment (P value)
Training	99.5 (99.2-99.8)	99.8 (99.6-100.0)	99.3 (98.9-99.7)	0.99 (0.98-1.00)	99.5 (99.2-99.8)	< 0.001
Testing	99.2 (98.8-99.6)	100.0 (99.9-100.0)	98.7 (98.1-99.3)	0.98 (0.97-0.99)	99.3 (98.9-99.7)

P < 0.001 vs manual assessment. The training set contains 58240 valid image patches, and the test set contains 31104 valid image patches. The manual assessment accuracy was 86.3%, with a kappa coefficient of 0.72. CI: Confidence interval.