Table 1.

Summary of the anomaly detection results. We have compared HealthyGAN with 6 state-of-the-art anomaly detection methods using 5 metrics on 3 medical imaging datasets. The best results are in bold and the second best results are underlined.

Datasets	Metrics	ALAD	ALOCC	f-AnoGAN	Ganomaly	Padim	PatchCore	HealthyGAN
COVID-19	AUC	0.58	0.63	0.64	0.58	0.56	0.52	0.84
	Prec.	0.49	0.63	0.55	0.59	0.56	0.52	0.76
	Rec.	0.89	0.63	0.53	0.60	0.56	0.53	0.76
	Spec.	0.09	0.63	0.56	0.59	0.56	0.51	0.76
	F1	0.64	0.63	0.54	0.60	0.56	0.52	0.76
X-ray 14 diseases	AUC	0.53	0.48	0.55	0.49	0.54	0.53	0.56
	Prec.	0.53	0.48	0.55	0.49	0.54	0.53	0.55
	Rec.	0.53	0.48	0.55	0.49	0.54	0.53	0.55
	Spec.	0.53	0.48	0.55	0.49	0.54	0.53	0.55
	F1	0.53	0.48	0.55	0.49	0.54	0.53	0.55
Migraine	AUC	0.60	0.40	0.50	0.70	0.35	0.60	0.75
	Prec.	0.60	0.40	0.50	0.70	0.36	0.60	0.78
	Rec.	0.60	0.40	0.50	0.70	0.40	0.60	0.70
	Spec.	0.60	0.40	0.50	0.70	0.30	0.60	0.80
	F1	0.60	0.40	0.50	0.70	0.38	0.60	0.74