# Polyp Generalization via Diversifying Style at Feature-Level Space
## 4 Results
### Experimental Results of CVC-ClinicDB  
To assess the efficacy of our proposed method, we trained it on the CVC-ClinicDB dataset and evaluated its performance on the remaining datasets, including its own test set. It is important to note that we utilized the same number of style images as those used for training on Kvasir-SEG. The results are presented in Table 7. Our proposed method demonstrates superior accuracy on unseen datasets compared to prior methods. It is noteworthy that all methods perform well when the training and testing samples are from the same source. However, we observed performance drops, particularly on the CVC-ColonDB, ETIS and KvasirCapsule datasets. For instance, DCRNet, ACSNet, PraNet and  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Method** & **U-Net** & **UNet++** & **DCRNet** & **ACSNet** & **PraNet** & **HardNet** & **FRCNet** & **MKDCNet** & **TransFuse** & **SwinE-Net** & **NSSM** \\ \hline mDice & 0.512 & 0.483 & 0.704 & 0.716 & 0.712 & 0.735 & 0.741 & 0.367 & 0.773 & 0.804 & **0.808** \\ mIoU & 0.444 & 0.41 & 0.631 & 0.649 & 0.64 & 0.666 & 0.67 & 0.296 & 0.696 & 0.725 & **0.730** \\ FM & 0.498 & 0.467 & 0.684 & 0.697 & 0.699 & 0.724 & 0.728 & 0.351 & N/A & 0.787 & **0.791** \\ SM & 0.712 & 0.691 & 0.821 & 0.829 & 0.82 & 0.834 & 0.831 & 0.627 & N/A & 0.869 & **0.871** \\ maxE & 0.776 & 0.76 & 0.848 & 0.851 & 0.072 & 0.875 & 0.878 & 0.766 & N/A & 0.910 & **0.915** \\ MAE & 0.061 & 0.064 & 0.052 & 0.039 & 0.043 & 0.038 & 0.036 & 0.103 & N/A & 0.028 & **0.026** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative result comparison of the proposed method on the CVC-ColonDB 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.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Method** & **U-Net** & **UNet++** & **DCRNet** & **ACSNet** & **PraNet** & **HardNet** & **FRCNet** & **MKDCNet** & **TransFuse** & **SwinE-Net** & **NSSM** \\ \hline mDice & 0.398 & 0.401 & 0.556 & 0.578 & 0.628 & 0.70 & 0.712 & 0.432 & 0.733 & 0.758 & **0.779** \\ mIoU & 0.335 & 0.344 & 0.496 & 0.509 & 0.567 & 0.63 & 0.647 & 0.371 & 0.659 & 0.687 & **0.694** \\ FM & 0.366 & 0.390 & 0.506 & 0.560 & 0.60 & 0.671 & 0.682 & 0.409 & N/A & 0.726 & **0.739** \\ SM & 0.684 & 0.683 & 0.736 & 0.754 & 0.794 & 0.828 & 0.837 & 0.679 & N/A & 0.864 & **0.853** \\ maxE & 0.74 & 0.776 & 0.773 & 0.764 & 0.808 & 0.89 & 0.89 & 0.745 & N/A & 0.902 & **0.904** \\ MAE & 0.036 & 0.035 & 0.096 & 0.059 & 0.031 & 0.015 & 0.015 & 0.061 & N/A & 0.012 & **0.011** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative result comparison of the proposed method on the ETIS 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.  
HardNet-MSEG exhibit impressive performance when tested on their own testing sets but struggle on other unseen datasets. In contrast, our proposed method consistently improves performance across all datasets except for Kvasir-SEG. Although PraNet achieves a Dice score of 0.876 and a mean IoU of 0.832, outperforming our proposed method in this specific dataset, this is not the case for other datasets.