# Polyp Generalization via Diversifying Style at Feature-Level Space
## 5 Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**CVC-ClinicDB**} & \multicolumn{3}{c}{**Kvasir-SEG**} & \multicolumn{3}{c}{**CVC-ColonDB**} & \multicolumn{3}{c}{**ETIS**} & \multicolumn{3}{c}{**Kvasir Capsule**} \\ \cline{2-13}  & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** \\ \hline U-Net & 0.874 & 0.806 & 0.748 & 0.682 & 0.586 & 0.527 & 0.557 & 0.419 & 0.358 & 0.301 \\ UNet++ & 0.886 & 0.835 & 0.752 & 0.689 & 0.604 & 0.548 & 0.603 & 0.517 & 0.416 & 0.349 \\ DCRNet & 0.930 & 0.876 & 0.793 & 0.715 & 0.694 & 0.625 & 0.397 & 0.338 & 0.701 & 0.630 \\ MKDCNet & 0.887 & 0.832 & 0.815 & 0.772 & 0.723 & 0.676 & 0.613 & 0.506 & 0.597 & 0.516 \\ ACSNet & 0.914 & 0.895 & 0.845 & 0.724 & 0.738 & 0.684 & 0.617 & 0.536 & 0.646 & 0.572 \\ PraNet & 0.915 & 0.867 & **0.876** & **0.832** & 0.716 & 0.644 & 0.630 & 0.575 & 0.737 & 0.664 \\ HardNet-MSEG & 0.924 & 0.891 & 0.840 & 0.73 & 0.712 & 0.634 & 0.652 & 0.574 & 0.541 & 0.437 \\
**Proposed Method (NSSM)** & **0.943** & **0.898** & 0.842 & 0.764 & **0.756** & **0.692** & **0.681** & **0.606** & **0.763** & **0.676** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed method trained on CVC-ClinicDB and tested on other datasets. the use of medically irrelevant transformations with natural images and demonstrated their superiority over previous approaches. This could be attributed to the potential of diverse transformations enabled by employing a wider range of style features, unlike medically relevant transformations, which are inherently limited. Our proposed methods, particularly the SGM and NSSM, align with this concept. We achieved style diversification by leveraging target images solely from different polyp datasets and generating plausible styles similar to the source image. Learning features that are specific to classes and independent of domains is crucial for deep learning models, akin to human cognition. Utilizing style transfer techniques at the feature space level with extensive diversification can enhance the model's representation significantly. While data augmentation at the image level is recognized as an effective technique for enhancing the performance and generalization of deep learning models, its application and potential in medical imaging remain largely unexplored, thus warranting further investigation. Additionally, determining an optimal configuration for data augmentation methods can vary depending on the datasets being utilized. As suggested by our proposed method, employing style data augmentation at the feature space level holds promise for learning domain-agnostic and class-specific representations. The findings presented in Table 1 underscore the need for future research to explore optimal settings for data augmentation and style transformation in a diverse and plausible manner, akin to the approach proposed in the NSSM, alongside existing methods. In clinical scenarios, the performance of deep learning models often diminishes due to variations in domain shift. Models that can generalize across multiple datasets are highly advantageous. While it is commonly believed that training deep learning models on diverse multi-institutional datasets can facilitate generalization to unseen datasets, our proposed method demonstrates that a well-curated dataset or thoughtfully designed architecture can compel the model to learn features that are both class-specific and invariant to domain shifts. Specifically, the NSSM achieved comparable performance within its dataset but exhibits superior performance on other unseen datasets. For instance, when trained on Kvasir-SEG and tested on CVC-ClinicDB, the NSSM achieved the highest Dice score of 0.933 and a mean IoU of 0.893. Similar performance gains are observed on the CVC-ColonDB and ETIS datasets.