# An Empirical Study on Ensemble of Segmentation Approaches
## 3 Results
### Experiments
#### 3.3.2 Ablation Studies
Some further ensembles are reported in Table 4:  
* \(PVT(2)\), sum rule between PVT combined with DA1 and PVT combined with DA2;
* \(FH(2)\), sum rule among two H_S (one combined with DA1, the latter with DA2) and two H_A (one combined with DA1, the latter with DA2);
* \(FH(2)\) + 2 \(\times\)\(PVT(2)\), weighted sum rule between \(PVT(2)\) and \(FH(2)\), the weight of \(PVT(2)\) is assigned so that its importance in the ensemble is the same as \(FH(2)\) (notice that \(FH(2)\) consists of four networks while \(PVT(2)\) is built by only two networks);
* \(ELossMix(10)\) + \((10/4)\) \(\times\)\(FH(2)\) + \((10/2)\) \(\times\)\(PVT(2)\), weighted sum rule among \(ElossMix(10)\), \(FH(2)\), and \(PVT(2)\); as in the previous ensemble, the weights are assigned so that each ensemble member has the same importance (notice that \(ElossMix(10)\) is the fusion by sum rule of 10 DeepLabV3+). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **DA** & **POLYP** & **SKIN** & **LEUKO** & **BFLY** & **EMICRO** & \(AVG\) \\ \hline _ELossMix(10)_ & DA1/DA2 & 0.851 & 0.883 & 0.936 & 0.983 & 0.924 & 0.915 \\ _H\_A_ & DA1 & 0.840 & 0.867 & 0.923 & 0.977 & 0.914 & 0.904 \\ _H\_A_ & DA2 & 0.854 & 0.871 & 0.945 & 0.982 & 0.912 & 0.913 \\ _H\_S_ & DA1 & 0.816 & 0.872 & 0.889 & 0.969 & 0.894 & 0.880 \\ _H\_S_ & DA2 & 0.847 & 0.870 & 0.917 & 0.976 & 0.901 & 0.902 \\ _FH_ & DA1 & 0.859 & 0.879 & 0.913 & 0.980 & 0.915 & 0.909 \\ _FH_(2) & DA1/2 & 0.862 & 0.885 & 0.934 & 0.982 & 0.916 & 0.916 \\ _PVT_ & DA1 & 0.854 & 0.878 & 0.954 & 0.975 & 0.920 & 0.916 \\ _PVT_ & DA2 & 0.855 & 0.879 & 0.954 & 0.984 & 0.919 & 0.918 \\ _PVT_(2) & DA1/2 & 0.855 & 0.883 & 0.957 & 0.984 & 0.922 & 0.920 \\ _FH(2)_ + 2 \(\times\)\(PVT(2)\) & DA1/2 & **0.875** & 0.892 & **0.955** & **0.985** & 0.924 & **0.926** \\ _ELossMix(10)_ + \((10/4)\)\(\times\)\(FH(2)\) + \((10/2)\)\(\times\)\(PVT(2)\) & DA1/2 & **0.875** & **0.893** & 0.953 & **0.985** & **0.926** & **0.926** \\ _ELossMix_2(10)_ + \((10/4)\)\(\times\)\(FH(2)\) + \((10/2)\)\(\times\)\(PVT(2)\) & DA1/2 & **0.874** & **0.893** & 0.954 & **0.985** & **0.926** & **0.926** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance (dice) of some stand-alone methods and ensembles in the five benchmark datasets. Best performance is marked in bold font. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **LOSS** & **POLYP** & **SKIN** & **LEUKO** & **BFLY** & **EMICRO** & \(AVG\) \\ \hline _RN101_ & L\({}_{\rm GD}\) & 0.808 & 0.871 & 0.915 & 0.976 & 0.918 & 0.898 \\ _RN101_ & L\({}_{\rm STR}\) & 0.809 & 0.869 & 0.930 & 0.964 & 0.901 & 0.895 \\ _RN101_ & L\({}_{\rm BoundExpS}\) & 0.803 & 0.874 & 0.928 & 0.978 & 0.901 & 0.897 \\ _RN101_ & L\({}_{\rm DeepRes}\) & 0.819 & 0.869 & 0.922 & 0.969 & 0.904 & 0.897 \\ _RN101_ & Comb4 & 0.813 & 0.860 & 0.920 & 0.972 & 0.920 & 0.897 \\ _RN101_ & L\({}_{\rm CS}\) & 0.823 & 0.873 & 0.917 & 0.967 & 0.911 & 0.898 \\ _ERN101(10)_ & L\({}_{\rm GD}\) & 0.834 & 0.878 & 0.925 & 0.978 & 0.919 & 0.907 \\ _ELoss101(10)_ & Many loss & 0.842 & 0.880 & 0.925 & 0.980 & 0.921 & 0.910 \\ _ELossMix_(10) & Many loss & **0.851** & **0.883** & 0.936 & 0.983 & **0.924** & 0.915 \\ _ELossMix_2(10) & Many loss & **0.851** & **0.883** & 0.943 & **0.984** & **0.925** & **0.917** \\ _ELossLarge_(10) & Many loss & 0.848 & **0.883** & **0.944** & **0.984** & 0.922 & **0.916** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance (dice) of some stand-alone methods and ensembles in the five benchmark datasets when varying the loss function; the last column \(AVG\) reports the average performance. Best performance is marked in bold font.