# Polyp Generalization via Diversifying Style at Feature-Level Space
## 4 Results
### Experimental Results of Kvasir-SEG
#### 4.2.2 Generalizability on CVC-ColonDB  
For the experimental evaluation of the generalization task, CVC-ColonDB serves as the third dataset, with only Kvasir-SEG utilized for training. The quantitative results presented in Table 4 demonstrate that our proposed method outperforms previous approaches across all evaluation metrics. Notably, the NSSM achieves outstanding scores with a Dice coefficient of 0.808, mean IoU of 0.73 and MAE of 0.026. FRCNet, HardNet and ACSNet also attain competitive scores, with MAE values of 0.036, 0.038 and 0.039, respectively.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Method** & **U-Net** & **UNet++** & **DCRNet** & **ACSUet** & **PraNet** & **HardNet** & **FRCNet** & **MKDCNet** & **TransFuse** & **SwinE-Net** & **NSSM** \\ \hline mDice & 0.818 & 0.821 & 0.886 & 0.898 & 0.898 & 0.897 & 0.915 & 0.888 & 0.918 & 0.920 & **0.920** \\ mIoU & 0.746 & 0.743 & 0.825 & 0.838 & 0.840 & 0.839 & 0.849 & 0.826 & 0.868 & **0.891** & 0.867 \\ FM & 0.794 & 0.808 & 0.865 & 0.882 & 0.885 & 0.885 & 0.911 & 0.75 & N/A & **0.913** & 0.910 \\ SM & 0.858 & 0.862 & 0.911 & 0.92 & 0.915 & 0.912 & 0.919 & 0.830 & N/A & **0.926** & 0.924 \\ maxE & 0.893 & 0.91 & 0.941 & 0.952 & 0.948 & 0.948 & 0.959 & 0.903 & N/A & N/A & **0.959** \\ MAE & 0.055 & 0.048 & 0.035 & 0.032 & 0.03 & 0.028 & 0.024 & 0.052 & N/A & 0.024 & **0.022** \\ Params & 31.38 & 9.16 & 28.99 & 29.45 & 32.50 & 33.34 & **0.78** & 19.84 & 26.30 & - & 30.60 \\ FPS & 41.04 & 30.67 & **35** & 34.82 & 48.25 & 85.3 & - & 47.54 & 98.7 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result comparison of the proposed method on the Kvasir-SEG. Note that mDice, mIoU, FM, SM, maxE and MAE represent mean dice coefficient, mean intersection over union, mean weighted F-measure, structure measure [39], max enhanced-alignment measure and mean absolute error, respectively. Evaluation scores of FRCNet [41], TransFuse [42] and SwinE-Net [43] are referred from their own research whereas evaluation scores for U-Net [21], UNet++ [22], DCRNet [28], ACSNet [26], PraNet [23], HardNet-MSEG [24] and MKDCNet [29] are computed.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Method** & **U-Net** & **UNet++** & **DCRNet** & **ACSUet** & **PraNet** & **HardNet** & **FRCNet** & **MKDCNet** & **TransFuse** & **SwinE-Net** & **NSSM** \\ \hline mDice & 0.633 & 0.635 & 0.787 & 0.837 & 0.901 & 0.763 & 0.933 & 0.824 & 0.918 & **0.938** & 0.881 \\ mIoU & 0.543 & 0.547 & 0.721 & 0.826 & 0.857 & 0.693 & 0.886 & 0.746 & 0.868 & **0.892** & 0.798 \\ FM & 0.711 & 0.725 & 0.774 & 0.873 & 0.896 & 0.935 & 0.915 & 0.529 & N/A & **0.936** & 0.935 \\ SM & 0.789 & 0.793 & 0.860 & 0.927 & 0.935 & 0.849 & 0.942 & 0.715 & N/A & **0.950** & 0.947 \\ maxE & 0.854 & 0.831 & 0.896 & 0.959 & 0.957 & 0.878 & 0.981 & 0.828 & N/A & **0.989** & 0.985 \\ MAE & 0.039 & 0.038 & 0.029 & 0.011 & 0.009 & 0.030 & 0.007 & 0.060 & N/A & **0.006** & 0.010 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result comparison of the proposed method trained on Kvasir-SEG and tested on the CVC-ClinicDB dataset. Evaluation scores of FRCNet [41], TransFuse [42] and SwinE-Net [43] are referred from their own research whereas evaluation scores for U-Net [21], UNet++ [22], DCRNet [28], ACSNet [26], PraNet [23], HardNet-MSEG [24] and MKDCNet [29] are computed.