Table 1.

Comparison of sensitivity and specificity for different methods on the COVID-19 recognition task on image basis. The best performance is boldfaced, and “sd” denotes standard deviation

Method		UP-ResNet18	P-ResNet18	UP-ResNet50	P-ResNet50
Sensitivity, mean (sd) (%)	COVID-19	90.23 (9.62)	97.11 (1.05)	90.87 (8.00)	96.72 (11.59)
	Other pneumonias	94.69 (5.65)	95.87 (4.95)	95.24 (4.05)	95.46 (4.06)
	Normal	93.3 (4.43)	93.83 (4.60)	92.81 (3.48)	96.65 (2.66)
Specificity, mean (sd) (%)	COVID-19	96.34 (3.55)	97.50 (2.15)	97.86 (1.40)	98.53 (2.04)
	Other pneumonias	96.82 (1.80)	96.64 (2.60)	94.97 (2.19)	97.44 (2.00)
	Normal	96.00 (5.61)	99.04 (0.75)	96.64 (4.08)	98.49 (0.79)

Sensitivity: $N_{x,tp}/(N_{x,tp}+N_{x,fn})$. Specificity: $N_{x,tn}/(N_{x,tn}+N_{x,fp})$

N the number of images, x the category, tp true positive, fp false positive, tn true negative, fn false negative