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Table 3 WM detection results for synthetic BrainWeb data

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

Noise Inhomogeneity ϕAcc(F) ϕAcc(X MDS ) ϕAcc(X GE ) ϕ A c c ( 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
  1. 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 ( X ̃ G E ) (with median as MLE). Improvements in classification accuracy via Ψ ( X ̃ G E ) were found to be statistically significant.