## 5 Discussion
### Qualitative Results Analysis  
As presented in Figures 9 and 10, the segmentation results predicted by the proposed SemiPolypSeg and other approaches using 15% labeled images on the Kvasir-SEG and CVC-ClinicDB datasets are provided. The baseline model (supervised-only), trained with fully supervised learning using only 15% of labeled data, shows the least accurate segmentation. The results are more fragmented and less reliable, highlighting the limitations of relying solely on labeled data. In contrast, the other semi-supervised segmentation methods, including MT, CCT, UCMT, and our proposed SemiPolypSeg, not only utilized the same proportion of labeled data but also leveraged the information from unlabeled data, resulting in better segmentation accuracy compared to the baseline. In Figure 9, we can see that on the Kvasir-SEG dataset, using 15% labeled images, the segmentation performance varied across different methods. While MT showed reasonable segmentation, it often struggled with accurately capturing the finer details of the polyp boundaries, resulting in less precise segmentation, with some areas showing over-segmentation or under-segmentation. Although the method's reliance on consistency between teacher and student models helped, it was not sufficient for complex polyp shapes. Figure 8: Training and validation curves for the DSC criterion as a function of the number of epochs on CVC-ClinicDB with 30% labeled images. Dark blue denotes the training curve, while orange highlights the validation curve. In contrast, CCT provided better segmentation than MT, with improved edge detection and overall segmentation quality; however, it still fell short in terms of edge smoothness and overlap with the ground truth. The segmentation results were less consistent, especially in regions with complex textures or irregular shapes. Similarly, UCMT performed comparably to CCT but tended to produce noisier segmentation results, with more irregular edges and occasional misclassifications. This method benefited from unsupervised consistency but struggled with maintaining smooth and accurate boundaries. On the other hand, SemiPolypSeg achieved more accurate delineation of polyp boundaries, reducing false positives and negatives. The method's ability to maintain the integrity of the polyp shape, even with limited labeled data, highlights its robustness and effectiveness. Consequently, the segmentation results are smoother and show a higher overlap with the ground truth, particularly at the lesion edges. Similarly, Figure 10 presents the results on the CVC-ClinicDB dataset, highlighting comparable trends with some dataset-specific observations. On the CVC-ClinicDB dataset, MT again showed reasonable performance but lacked the precision and smoothness of SemiPolypSeg. The segmentation results are less accurate, particularly at the lesion edges, where MT struggled to capture the fine details. Although CCT provided better segmentation than MT, with improved edge detection and overall segmentation quality,  
Figure 10: Qualitative results of different methods on CVC-ClinicDB. Blue denotes the regions where the prediction aligns with the ground truth, while red highlights the areas where the prediction diverges from the ground truth. Figure 9: Qualitative results of different methods on Kvasir-SEG. Blue denotes the regions where the prediction aligns with the ground truth, while red highlights the areas where the prediction diverges from the ground truth. it still exhibited some inconsistencies, especially in areas with complex textures. The method showed better performance than MT but was still not as smooth or accurate as SemiPolypSeg. Similarly, UCMT's performance is comparable to CCT, with slightly better edge detection but more noise in the segmentation results. While the method showed some improvement in capturing the lesion boundaries, it still produced irregular edges and occasional misclassifications. On the other hand, SemiPolypSeg outperformed other methods by providing clearer and more precise segmentation.