Table 4.

Testing result (Dataset II) of the four classifiers trained with the features extracted after fine-tuning of the five different deep CNNs. NI-to-PI: transferring from the natural image dataset to the target pathology image dataset; and PI-to-PI: transferring from the non-target PI dataset to the target PI dataset. All three networks were fine-tuned on Dataset II. C1: first convolutional layer; FC1: fully connected layer. Each classifier was built on the C3 layer for the C1-C2-C3-FC1 network and on the C5 layer for the C1-C2-C3-C4-C5-FC1 network.

Score╲Model		C1-C2-C3-FC1		C1-C2-C3-C4-C5-FC1		AlexNet		Places365-AlexNet		GoogLeNet
		NI-to-PI	PI-to-PI	NI-to-PI	PI-to-PI	NI-to-PI	PI-to-PI	NI-to-PI	PI-to-PI	NI-to-PI	PI-to-PI
ACC (%)	SVClnr	89.9	89.8	89.3	89.5	88.7	87.8	88.5	88.8	91.8	91.3
	SVCrbf	75.0	73.5	90.3	90.4	91.3	90.8	91.5	91.4	90.8	90.6
	RF	88.3	88.1	86.1	87.6	90.3	89.7	90.0	90.3	90.1	90.3
	KNN	89.0	88.8	87.0	88.6	90.8	91.0	91.2	90.8	91.5	91.3
AUC	SVClnr	0.966	0.965	0.959	0.962	0.958	0.954	0.959	0.959	0.974	0.973
	SVCrbf	0.919	0.922	0.964	0.965	0.973	0.972	0.973	0.973	0.970	0.970
	RF	0.947	0.951	0.937	0.945	0.961	0.958	0.959	0.959	0.961	0.961
	KNN	0.958	0.959	0.950	0.958	0.970	0.969	0.971	0.970	0.973	0.974
TPR (%)	SVClnr	88.9	88.5	88.3	88.2	88.3	88	88.3	88.5	91.5	90.8
	SVCrbf	53.7	49.5	89.3	88.4	90.2	88.6	90.7	90.7	90.6	89.5
	RF	85.8	84.5	83.4	85.1	88.3	88	88.0	88.7	87.9	88.6
	KNN	89.1	87.9	87.3	86.1	88	89.4	89.8	89.7	90.4	89.6
PPV (%)	SVClnr	90.8	90.9	90.2	90.6	89.1	87.7	88.7	89.1	92.1	91.8
	SVCrbf	94.0	95.7	91.2	92.1	92.3	92.7	92.2	92.0	91.0	91.6
	RF	90.4	91.1	88.3	89.7	92.0	91.2	91.7	91.7	92.0	91.8
	KNN	89.0	89.6	86.9	90.7	93.3	92.4	92.4	91.8	92.5	92.8
F1 (%)	SVClnr	89.8	89.8	89.2	89.4	88.7	87.9	88.5	88.9	91.9	91.3
	SVCrbf	68.3	65.3	90.3	90.2	91.3	90.6	91.5	91.4	90.9	90.6
	RF	88.0	87.7	85.8	87.3	90.1	89.5	89.9	90.2	90.0	90.2
	KNN	89.1	88.7	87.1	88.3	90.6	90.9	91.1	90.8	91.5	91.2