Table 4 Performance result of using active learning.

	Iteration 1		Iteration 2		Iteration 3		Iteration 4		Iteration 5		Iteration 6		Iteration 7		Iteration 8
Object class	Count	AP	Count	AP	Count	AP	Count	AP	Count	AP	Count	AP	Count	AP	Count	AP
Neutrophil	680	0.75	1256	0.82	1568	0.83	1756	0.85	1895	0.86	2050	0.89	2398	0.91	2714	0.90
Metamyelocyte	480	0.60	605	0.66	752	0.69	785	0.72	856	0.76	925	0.75	986	0.76	1017	0.77
Myelocyte	390	0.53	589	0.55	665	0.59	720	0.62	869	0.70	950	0.78	1015	0.79	1199	0.80
Promyelocyte	65	0.44	102	0.46	256	0.52	285	0.54	320	0.59	326	0.62	360	0.64	409	0.62
Blast	1050	0.69	1785	0.76	2029	0.78	2590	0.81	2896	0.80	3268	0.83	3526	0.84	3950	0.84
Erythroblast	620	0.72	1150	0.78	1390	0.80	1580	0.82	2028	0.89	2295	0.90	2480	0.92	2668	0.92
Megakaryocyte nucleus	5	0.32	7	0.35	18	0.52	19	0.55	19	0.55	23	0.60	23	0.59	23	0.60
Lymphocyte	390	0.47	530	0.48	689	0.50	706	0.51	780	0.52	1015	0.59	1150	0.62	1305	0.66
Monocyte	62	0.47	98	0.51	295	0.57	368	0.61	423	0.62	485	0.65	520	0.68	569	0.72
Plasma cell	29	0.57	45	0.59	50	0.61	82	0.63	105	0.67	135	0.68	158	0.71	176	0.72
Eosinophil	31	0.59	38	0.63	135	0.83	172	0.86	185	0.88	221	0.95	228	0.95	249	0.97
Megakaryocyte	25	0.49	30	0.52	90	0.77	90	0.77	92	0.78	95	0.80	100	0.81	106	0.82
Debris	1380	0.58	2680	0.62	3450	0.65	3920	0.68	4490	0.73	4901	0.77	5260	0.77	5603	0.79
Histiocyte	38	0.34	72	0.42	147	0.48	163	0.48	168	0.51	174	0.52	182	0.54	191	0.54
Platelet	790	0.41	1680	0.46	2150	0.48	2560	0.52	2890	0.58	3250	0.65	3680	0.65	3971	0.64
Platelet clump	93	0.37	146	0.41	320	0.54	409	0.56	475	0.57	536	0.58	563	0.61	585	0.62
Average	6128	0.52	10813	0.56	14004	0.64	16205	0.66	18491	0.69	20649	0.72	22629	0.74	24735	0.75

Model training started with a small dataset at the first and second iteration, and then is improved (especially on rare cellular objects) in the subsequent iterations by using active learning.

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