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. Author manuscript; available in PMC: 2021 Oct 1.
Published in final edited form as: Ultrasound Med Biol. 2020 Jul 21;46(10):2819–2833. doi: 10.1016/j.ultrasmedbio.2020.06.015

Table 3:

Values of the performance metrics for tumor segmentation by different models. The shown values correspond to the average and standard deviation (in parenthesis) per fold in five-fold cross-validation. LR represents the used learning rates for training the models.

Model Training Setting DSC JI (IOU) TPR FPR ACC AUC-ROC
Seg-Net LR=8·10−4, decreased by 0.5 after 10 epochs until 1·10−4 0.889 (±0.011) 0.811 (±0.015) 0.877 (±0.019) 0.088 (±0.014) 0.977 (±0.002) 0.957 (±0.004)
DenseNet-26 LR=1·10−3, decreased by 0.1 after 10 epochs until 1·10−4 0.888 (±0.016) 0.818 (±0.017) 0.886 (±0.019) 0.093 (±0.025) 0.978 (±0.002) 0.958 (±0.005)
PSPNet-ResNet18 LR=1·10−4, decreased by 0.5 after 10 epochs until 5·10−5, images size of 384×384 pix. 0.886 (±0.008) 0.808 (±0.008) 0.884 (±0.014) 0.107 (±0.016) 0.976 (±0.002) 0.953 (±0.005)
Ours: U-Net-SA LR = 1·10−4 0.901 (±0.013) 0.832 (±0.014) 0.904 (±0.016) 0.092 (±0.008) 0.979 (±0.001) 0.955 (±0.002)