## 5 Conclusions  
This study proposes a novel method for colon polyp segmentation called WSPolypSAM, which utilizes weak annotations to guide SAM in generating pseudo-labels for self-guided fine-tuning, reducing the dependence on precise annotation data. The experimental results demonstrate the competitive performance of WSPolyp-SAM in colon polyp segmentation compared to current fully supervised learning methods, bringing a new technological breakthrough to this field. Additionally, through experiments on the segmentation performance of the SAM model under different versions of the ViT and comparing different amounts of training data under different supervision strategies (weakly supervised vs. fully supervised), several key findings can be obtained. Figure 5: Comparison between weakly supervised and fully supervised fine-tuning for different percentages of supervision mask. The stars indicate that the performance of the weakly supervised strategy with 100% of the training data is comparable to that of the fully supervised strategy. Firstly, despite the higher computational complexity of SAM-L, its performance on most public datasets is not superior to the lightweight SAM-B. This finding demonstrates that SAM-B is more suitable for efficient deployment in medical imaging applications. Secondly, under the weakly supervised strategy, using 100% of the training data can achieve training results of 56% to 62% compared to fully supervised learning. This means that 70% to 73% of the annotation time costs can be saved. These findings not only affirm the technical efficacy of our proposed approach but also emphasize its potential value in reducing annotation costs and improving work efficiency. However, our research is not without its limitations. (1) Pseudo-label quality control: The qualities of the pseudo-labels used in the model fine-tuning process of WSPolyp-SAM are inevitably affected by the coarse segmentation masks. In the future, we will study more robust methods to identify error-prone areas and then refine the poor masks, which are expected to alleviate this problem. (2) Multi-class polyp identification: As WSPolyp-SAM uses the same mask decoder as SAM, it is currently unable to distinguish between different types of polyps, which is crucial for clinical management strategies. We hope to expand our method to recognize different types of polyps by introducing more information like the polyp's shape and visual appearance into WSPolyp-SAM and designing a new mask decoder supporting multi-class prediction in the future. (3) Optimization of real-time segmentation performance: In clinical colonoscopy, real-time segmentation is essential for quick diagnosis and treatment. Actually, the proposed WSPolyp-SAM is flexible to fulfill this demand by adopting more lightweight visual backbones, such as the tiny ViT used in MobileSAM. Additionally, in practical applications, the inference speed can be further boosted by exploiting low-bit inference, model compression, and quantization techniques. We believe that these improvements will make WSPolyp-SAM a more comprehensive tool, further enhancing its performance and functionality in the task of polyp segmentation. Conceptualization, T.C., H.Y. and K.D. ; methodology, T.C., H.Y. and K.D. ; software, T.C. ; validation, T.C. ; resources, T.C. ; data curation, T.C. ; supervision, H.Y. and K.D. ; writing--original draft preparation, T.C. ; writing--review and editing, T.C., H.Y., K.D., Y.Z. ([PERSON]) and Y.Z. ([PERSON]); funding acquisition, K.D. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China under Grant 62306310. Not applicable. The datasets analyzed during the current study are available at the link [[https://github.com/DengPingFan/PraNet](https://github.com/DengPingFan/PraNet)]([https://github.com/DengPingFan/PraNet](https://github.com/DengPingFan/PraNet)) (accessed on 9 August 2023).