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

Table 4 Ablation experiment performance indicators of the gastric-tumour-infiltrating lymphocytes-convolutional neural network model (independent test set)

Ablation experiment type	Experimental group	Accuracy (95%CI)	Specificity (95%CI)	Sensitivity (95%CI)	Cohen’s Kappa	P value (vs optimal group)
Feature fusion weight	Dynamic learning weight (0.4/0.3/0.3)	99.2 (98.8-99.6)	100.0 (99.9-100.0)	98.7 (98.1-99.3)	0.98	< 0.001
Feature fusion weight	Equal weight (0.33/0.33/0.33)	96.5 (95.8-97.2)	97.8 (97.1-98.5)	95.1 (94.2-96.0)	0.89
Input channel type	2-channel H/E (Colour Deconvolution)	99.2 (98.8~99.6)	100.0 (99.9~100.0)	98.7 (98.1-99.3)	0.98	< 0.001
Input channel type	3-channel RGB	94.7 (93.9-95.5)	92.3 (91.2-93.4)	95.1 (94.0-96.2)	0.87

P < 0.001 vs optimal group. The optimal group was the dynamic learning weight reassignment/2-channel haematoxylin/eosin group, which was the final model adopted. H/E: Haematoxylin/eosin; RGB: Red green blue (red, green, blue channels); CI: Confidence interval.