# Deep Ensembles Based on Stochastic Activations for Semantic Segmentation
## 4 Result on Skin Detection  
### Datasets, Testing Protocol, and Metrics  
To evaluate the proposed ensemble for image segmentation, we also performed a test on another relevant segmentation problem: skin detection. Skin segmentation (or detection) is a problem that discriminates regions in images and videos into the two classes \"skin\" and \"nonskin\". Following the testing framework developed in Reference [22], the performance results of the ensemble proposed here were compared to several state-of-the-art approaches on 11 datasets (Table 5) for skin segmentation. The training protocol provided that network models were trained only on the first 2000 images of the ECU dataset, while the other skin datasets were used only for testing (including the remaining 2000 images from ECU).  
The evaluation and comparison of the state-of-the-art approaches were performed according to the most used performance indicators in skin detection, F1-score, i.e., Dice, which is calculated at the pixel level (and not at the image level) to be independent of the image size in the different datasets (i.e. the values TP, TN, FP, and FN in the confusion matrix are calculated summing the values of all the pixels in the dataset and then the F1-score is calculated as in equation 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **IoU** & **Dice** & **F2** & **Prec.** & **Rec.** & **Acc.** \\ \hline resnet50-352 & 0.801 & 0.872 & 0.884 & 0.881 & 0.900 & 0.964 \\ resnet50-352\_sel & 0.825 & 0.888 & 0.892 & 0.915 & 0.902 & 0.967 \\ U-Net [17] & 0.471 & 0.597 & 0.598 & 0.672 & 0.617 & 0.894 \\ ResUNet [17] & 0.572 & 0.69 & 0.699 & 0.745 & 0.725 & 0.917 \\ ResUNet++ [17] & 0.613 & 0.714 & 0.72 & 0.784 & 0.742 & 0.917 \\ FCN8 [17] & 0.737 & 0.831 & 0.825 & 0.882 & 0.835 & 0.952 \\ HRNet [17] & 0.759 & 0.845 & 0.847 & 0.878 & 0.859 & 0.952 \\ DoubleUNet [17] & 0.733 & 0.813 & 0.82 & 0.861 & 0.84 & 0.949 \\ PSPNet [17] & 0.744 & 0.841 & 0.831 & 0.89 & 0.836 & 0.953 \\ DeepLabv3+ResNet50 [17] & 0.776 & 0.857 & 0.855 & 0.891 & 0.861 & 0.961 \\ DeepLabv3+ResNet101[17] & 0.786 & 0.864 & 0.857 & 0.906 & 0.859 & 0.961 \\ U-Net ResNet34 [17] & 0.81 & 0.876 & 0.862 & 0.944 & 0.86 & 0.968 \\ ColongSet [17] & 0.724 & 0.821 & 0.821 & 0.843 & 0.850 & 0.949 \\ DDANet [52] & 0.78 & 0.858 & — & 0.864 & 0.888 & — \\ HarDNet-MSEG [23] & 0.848 & 0.904 & 0.915 & 0.907 & 0.923 & 0.969 \\ \hline \hline \end{tabular}
\end{table}
Table 4: State-of-the-art approaches in the Kvasir dataset using the same testing protocol (all values are those reported in the reference papers, except for our approaches). The results of many methods are reported in [17]. Please refer to it for the original reference of a given approach. Other results [50,51] using a different protocol are not included in the comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ensemble Name** & **IoU** & **Dice** & **F2** & **Prec.** & **Rec.** & **Acc.** \\ \hline resnet18\_act & 0.774 & 0.856 & 0.856 & 0.888 & 0.867 & 0.955 \\ resnet18\_relu & 0.774 & 0.858 & 0.858 & 0.892 & 0.867 & 0.955 \\ resnet18\_sto & 0.780 & 0.860 & 0.857 & 0.898 & 0.864 & 0.956 \\ resnet50\_act & 0.779 & 0.858 & 0.859 & 0.894 & 0.869 & 0.957 \\ resnet50\_relu & 0.772 & 0.855 & 0.858 & 0.889 & 0.870 & 0.955 \\ resnet50\_sto & 0.779 & 0.859 & 0.864 & 0.891 & 0.877 & 0.957 \\ resnet50-352\_sto & 0.820 & 0.885 & 0.888 & **0.915** & 0.896 & 0.966 \\ resnet50-352\_sel & **0.825** & **0.888** & **0.892** & **0.915** & **0.902** & **0.967** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiments on ensembles in the Kvasir dataset (best results in bold).