## 6 Conclusions  
The labor-intensive nature of manual polyp image labeling makes it challenging to use fully supervised deep learning methods for polyp segmentation tasks that require large amounts of labeled data. In response, we introduce SemiPolypSeg, a semi-supervised segmentation framework that leverages cross-pseudo supervision and contrastive learning, thereby reducing the need for extensive labeled data. By learning from a broader set of unlabeled polyp images, this framework significantly reduces dependency on labeled images, thereby boosting model efficacy. Rigorous testing on the Kvasir-SEG and CVC-ClinicDB datasets confirms our framework's superiority, outperforming other leading semi-supervised algorithms with only a small amount of labeled images and achieving the highest scores. Notably, SemiPolypSeg surpassed the best DSC of competing algorithms by over 1% on Kvasir-SEG and 2% on CVC-ClinicDB with just 15% labeled data. Although our proposed method has shown promising results in semi-supervised polyp segmentation, we must acknowledge its limitations, especially when dealing with images captured in challenging polyp image acquisition environments. When dealing with blurry polyp images and images with uneven lighting during colonoscopy procedures, our model's segmentation accuracy significantly decreased. Furthermore, while SemiPolypSeg benefits from the inclusion of numerous unlabeled images, a certain amount of precisely labeled images remains essential. In the medical field, obtaining finely labeled images is often challenging. Therefore, research on methods that require minimal weakly labeled data would be highly valuable. Future research will focus on developing algorithms capable of precise polyp segmentation in difficult polyp image acquisition scenarios with minimal weakly labeled data.  
**Author Contributions:** Conceptualization, P.G. and G.L.; formal analysis, P.G.; investigation, P.G.; methodology, P.G. and G.L.; software, P.G.; writing--original draft, P.G.; writing--review and editing, G.L.; supervision, G.L.; resources, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is funded by the National Natural Science Foundation of China (Grants No. 42061055), the Science and Technology Research Projects of Jiangxi Province Education Department (Grants No. GJJ2201608), and the Project of Humanities and Social Sciences in Colleges and Universities of Jiangxi Province (Grants No. YS20129).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are openly available. The Kvasir-SEG can be found here: [[https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/)]([https://datasets.simula.no/kvasir-seg/](https://datasets.simula.no/kvasir-seg/)) (accessed on 26 August 2024). The CVC-ClinicDB can be found here: [[https://polyp.grand-challenge.org/CVClinicDB/](https://polyp.grand-challenge.org/CVClinicDB/)]([https://polyp.grand-challenge.org/CVClinicDB/](https://polyp.grand-challenge.org/CVClinicDB/)) (accessed on 26 August 2024).  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest.