Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

BMC Bioinformatics

Table 4 Comparing the mean and median as ML estimators within CalcConsEmbed

Noise	Inhomogeneity	$ϕ^{A c c} ({\tilde{X}}_{G E}^{M e d})$	$ϕ^{A c c} ({\tilde{X}}_{G E}^{M e a n})$
	0%	66.86 ± 2.89	66.89 ± 2.91
0%	20%	61.65 ± 4.58	65.34 ± 4.12
	40%	64.28 ± 5.93	63.39 ± 6.51
	0%	80.62 ± 1.03	80.45 ± 1.07
1%	20%	73.07 ± 8.97	77.81 ± 0.96
	40%	66.46 ± 9.80	70.56 ± 7.15
	0%	85.38 ± 0.75	85.53 ± 0.84
3%	20%	84.61 ± 0.81	84.49 ± 0.76
	40%	79.19 ± 7.56	81.37 ± 1.39
	0%	89.68 ± 1.36	90.85 ± 1.32
5%	20%	86.81 ± 1.38	87.01 ± 1.83
	40%	81.67 ± 1.51	81.82 ± 1.32
	0%	87.81 ± 0.73	86.17 ± 6.11
7%	20%	86.07 ± 1.05	82.73 ± 8.23
	40%	81.53 ± 1.57	81.72 ± 1.47
	0%	75.51 ± 14.35	74.32 ± 16.11
9%	20%	78.18 ± 9.86	73.63 ± 12.75
	40%	78.18 ± 9.86	73.63 ± 12.75

Pixel-level WM detection accuracy and standard error averaged over 10 MNI brain images and for 18 combinations of noise and inhomogeneity (180 experiments) with each of the 2 ML estimators considered in CalcConsEmbed: (1) median $(Ψ ({\tilde{X}}_{G E}^{M e d}))$ , (2) $(Ψ ({\tilde{X}}_{G E}^{M e a n}))$ .

ISSN: 1471-2105