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. 2020 Apr 20;15:87. doi: 10.1186/s13014-020-01514-6

Table 2.

Performance of the algorithms by network type and type of ensemble building. SUM: summation, MV: majority voting

DCNN Type Ensemble Sensitivity Precision F1-Score Sensitivity Small BM AFPR Mean DSC DSC
Method per Lesion
moU-Net SUM 0.71 0.89 0.79 0.51 0.18 0.71 0.74
cU-Net SUM 0.71 0.94 0.81 0.51 0.1 0.7 0.73
sU-Net SUM 0.53 0.85 0.65 0.68 0.2 0.27 0.61
NetSUM 0.82 0.83 0.82 0.7 0.35 0.7 0.74
moU-Net MV 0.65 0.96 0.78 0.43 0.05 0.71 0.73
cU-Net MV 0.63 1 0.77 0.4 0 0.69 0.73
sU-Net MV 0.43 0.95 0.59 0.62 0.05 0.21 0.52
NetMV 0.77 0.96 0.85 0.64 0.08 0.71 0.71

DSC dice similarity coefficient, AFPR average false positive rate, F1-score combines sensitivity and specificity into a single metric by calculation of their harmonic mean in order to find the most balanced model