Table 4.

Comparison of proposed active learning strategies and selection criteria. As measured by the Area under the Learning Curve (ALC), bolded values in the table indicate the outstanding learning strategies (see Table 2) using certain active selection criteria, and starred values represent the best performance taking both learning strategies and active selection criteria into consideration. For all three applications, we report baseline performance of random fine-tuning (RFT) using AlexNet in the table footnote. Considering the variance of random sampling for each active learning step, we conduct five independent trials for RFT and report the mean and standard deviation (mean±s.d.).

Application	Learning strategy	+ Diversity − Majority − Randomness	+ Diversity + Majority − Randomness	+ Diversity − Majority + Randomness	+ Diversity + Majority + Randomness	+ Entropy − Majority − Randomness	+ Entropy + Majority − Randomness	+ Entropy − Majority + Randomness	+ Entropy + Majority + Randomness
Colonoscopy frame classification	ACFT(Q)	0.8375	0.8773	0.8995	0.9160	0.8444	0.8227	0.9136	0.9061
	ACFT(LQ)	0.8501	0.8956	0.9083	0.9262	0.9149	0.9051	0.9033	0.9223
	AFT(LQ)	0.9183	0.9253	0.9299	0.9344	0.9219	0.9180	0.9268	0.9291
	ACFT(HQ)	0.9048	0.9236	0.9241	0.9179	0.9198	0.9266	0.9257	0.9293
Polyp detection	ACFT(Q)	0.8669	0.9023	0.8984	0.9168	0.8834	0.8656	0.9034	0.9271
	ACFT(LQ)	0.9195	0.9142	0.9497	0.9488	0.9204	0.9255	0.9475	0.9444
	AFT(LQ)	0.9242	0.9285	0.9353	0.9355	0.9292	0.9238	0.9367	0.9522
	ACFT(HQ)	0.9013	0.9370	0.9116	0.9363	0.9321	0.9436	0.9196	0.9443
Pulmonary embolism detection	ACFT(Q)	0.7828	0.7911	0.7690	0.7977	0.7855	0.7736	0.7296	0.7833
	ACFT(LQ)	0.8083	0.8176	0.7975	0.8263	0.8032	0.8086	0.8022	0.8245
	AFT(LQ)	0.7650	0.7973	0.7978	0.8040	0.7917	0.7878	0.7964	0.8222
	ACFT(HQ)	0.8272	0.7876	0.8047	0.8245	0.8218	0.7995	0.8155	0.8205

1 RFT in colonoscopy frame classification: ALC = 0.8958±0.0176 2 RFT in polyp detection: ALC = 0.9358±0.0130 3 RFT in pulmonary embolism detection: ALC = 0.7849±0.0261