# Polyp Generalization via Diversifying Style at Feature-Level Space
## 5 Discussion
Notably, the proposed method outperforms prior methods significantly, as evidenced in Table 6, where it attains a Dice score of 0.844 and a mean IoU of 0.750, surpassing other major metrics, while prior methods struggle with generalization. Similarly, when trained on CVC-ClinicDB and tested on other datasets, the proposed method achieves the highest scores on all metrics. Through extensive experimentation, the results indicate that the proposed method exhibits superior generalizability, attributable to its style augmentation approach at the feature space level, which consistently generates diverse style features while preserving key content features. We conducted ablation studies to determine the optimal settings. We experimented with different mixing ratios between the source training images and target style images at the feature space. Our findings indicate that an equal mixing ratio resulted in greater diversity in the features, leading to improved performance on unseen datasets. The quantitative results of these experiments are presented in Table 8.  
One significant limitation of our study is that we tested our approach solely on a simple UNet architecture. This decision was made to avoid potential interpretability issues that could arise from using more complex models. However, future research endeavors are necessary to investigate whether our approach can effectively address these limitations and demonstrate its robustness and efficiency in various scenarios. Specifically, it would be valuable to explore the applicability of our method within the frameworks of prior methods' backbones. Additionally, extending the evaluation to other domains such as classification and detection tasks, as well as different medical imaging domains including histopathology, dermatology and radiology, would provide further insights into the versatility of our approach. Another limitation worth mentioning is that our proposed method (the NSSM)requires longer training times compared to previous works, primarily due to the style augmentation being performed at the feature space level on the fly. In summary, we have introduced the NSSM, a novel style-based data augmentation method designed to learn diverse style features from a comprehensive set of medically relevant polyp images originating from various sources. Our approach aims to facilitate the acquisition of domain-agnostic and class-specific feature representations within the polyp domain. Through our experiments, we have demonstrated notable enhancements in the performance of segmentation tasks across different unseen datasets, particularly when confronted with domain shift challenges. Our investigation underscores two key findings. Firstly, CNNs exhibit a bias towards style features and may rely on low-level attributes such as color and texture, rendering them susceptible to domain shifts within polyp domains. Secondly, we posit that the incorporation of a medically relevant NSSM can serve as a practical strategy to alleviate this reliance, thereby offering a potential avenue for acquiring domain-agnostic representations. Data curation, S.P. and S.-W.L. ; Funding acquisition, S.-W.L. ; Methodology, S.P. ; Supervision, S.-W.L. ; Writing--original draft, S.P. ; Writing--review and editing, S.-W.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Gachon University research fund of 2019 (GCU-2019-0771) and the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. RS-2023-00250978). Not applicable. The data presented in this study are available on request. We would like to acknowledge and thank [PERSON] for aiding in data collection for the training of the model. The authors declare no conflicts of interest.