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

BMC Bioinformatics

Table 3 WM detection results for synthetic BrainWeb data

Noise	Inhomogeneity	ϕ^Acc(F)	ϕ^Acc(X_MDS)	ϕ^Acc(X_GE)	$ϕ^{A c c} ({\tilde{X}}_{G E})$
	0%	65.55 ± 1.84	65.55 ± 1.84	65.55 ± 1.84	66.86 ± 2.89
0%	20%	55.75 ± 1.65	55.75 ± 1.65	55.75 ± 1.65	61.65 ± 4.58
	40%	70.03 ± 2.79	70.08 ± 2.82	51.84 ± 0.99	64.28 ± 5.93
	0%	59.78 ± 1.31	59.74 ± 1.29	74.71 ± 9.06	80.62 ± 1.03
1%	20%	59.36 ± 1.30	59.32 ± 1.33	60.95 ± 8.67	73.07 ± 8.97
	40%	59.20 ± 1.12	59.12 ± 1.15	56.38 ± 1.53	66.46 ± 9.80
	0%	53.35 ± 1.31	53.39 ± 1.27	59.94 ± 7.00	85.38 ± 0.75
3%	20%	55.01 ± 2.92	54.91 ± 3.11	63.88 ± 10.85	84.61 ± 0.81
	40%	57.63 ± 1.78	57.71 ± 1.67	57.33 ± 1.38	79.19 ± 7.56
	0%	62.90 ± 0.72	62.84 ± 0.66	66.67 ± 10.22	89.68 ± 1.36
5%	20%	61.49 ± 1.38	61.49 ± 1.42	82.61 ± 7.39	86.81 ± 1.38
	40%	61.02 ± 0.99	61.03 ± 1.09	74.91 ± 9.09	81.67 ± 1.51
	0%	64.28 ± 0.71	64.26 ± 0.76	66.95 ± 6.25	87.81 ± 0.73
7%	20%	64.07 ± 1.03	64.01 ± 0.96	74.22 ± 10.59	86.07 ± 1.05
	40%	64.05 ± 1.19	64.04 ± 1.14	64.44 ± 1.25	81.53 ± 1.57
	0%	64.96 ± 0.90	64.94 ± 0.88	66.36 ± 1.66	75.51 ± 14.35
9%	20%	64.85 ± 0.97	64.79 ± 0.95	65.68 ± 1.32	78.18 ± 9.86
	40%	64.65 ± 0.83	64.63 ± 0.84	65.30 ± 0.74	77.83 ± 5.00

Pixel-level WM detection accuracy and standard error averaged over 10 MNI brain images and across 18 combinations of noise and inhomogeneity for each of: (1) Ψ(F), (2) Ψ(X_MDS), (3) Ψ(X_GE), (4) $Ψ ({\tilde{X}}_{G E})$ (with median as MLE). Improvements in classification accuracy via $Ψ ({\tilde{X}}_{G E})$ were found to be statistically significant.

ISSN: 1471-2105