## 4 Experiments
### Results
#### 4.3.4 Further Analysis
Additionally, the pseudo-label generation process requires multiple invocations of SAM, incurring high time costs, making it challenging for direct use in predictions. In comparison, using WSPolyp-SAM is more feasible. **Effectiveness of weakly supervised fine-tuning in reducing annotation costs. ** We further investigated the impact of different amounts of training data for supervision masks on the results of fine-tuned SAM, fully demonstrating SAM's efficiency in utilizing fine-tuning data. Figure 5 illustrates the comparison between weakly supervised and fully supervised approaches when the training data amount to between 10% and 100%, using the SAM-B model for fine-tuning. As the amount of data increases, the overall accuracy gradually improves. On the CVC-300 dataset, a weakly supervised strategy with 100% training data achieves results comparable to a fully supervised strategy with 90% to 100% training data. On the Kvasir-SEG dataset, a weakly supervised strategy with 100% training data achieves results comparable to a fully supervised strategy with 80% training data. On the ColonDB dataset, a weakly supervised strategy with 100% training data achieves results comparable to a fully supervised strategy with 50% training data. However, performance is relatively poorer on the other two datasets. On the ETIS dataset, a weakly supervised strategy with 100% training data achieves results comparable to a fully supervised strategy with 40% to 50% training data. On the ClinicDB dataset, a weakly supervised strategy with 100% training data only achieves results comparable to a fully supervised strategy with 20% to 30% training data. Overall, using 100% training data under the weakly supervised strategy achieves 56% to 62% of the training results of fully supervised learning. We simulated the \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Kvasir-SEG**} & \multicolumn{2}{c}{**ClinicDB**} & \multicolumn{2}{c}{**ColonDB**} & \multicolumn{2}{c}{**CVC-300**} & \multicolumn{2}{c}{**ETIS**} \\ \cline{2-11}  & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** \\ \hline SAM & 0.892 & 0.832 & 0.884 & 0.812 & 0.856 & 0.777 & 0.922 & 0.862 & 0.873 & 0.794 \\ Pseudo-label Generation & 0.899 & 0.844 & 0.912 & 0.844 & 0.890 & 0.814 & 0.932 & 0.876 & 0.884 & 0.810 \\ WSPolyp-SAM & 0.933 & 0.879 & 0.920 & 0.858 & 0.904 & 0.831 & 0.933 & 0.878 & 0.888 & 0.813 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between pseudo-label generation and WSPolyp-SAM. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model-ViT**} & \multirow{2}{*}{**Mask**} & \multicolumn{2}{c}{**Kvasir-SEG**} & \multicolumn{2}{c}{**ClinicDB**} & \multicolumn{2}{c}{**ColonDB**} & \multicolumn{2}{c}{**CVC-300**} & \multicolumn{2}{c}{**ETIS**} \\ \cline{2-11}  & & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** \\ \hline SAM-B & - & 0.892 & 0.832 & 0.884 & 0.812 & 0.856 & 0.777 & 0.922 & 0.862 & 0.873 & 0.794 \\ SAM-L & - & 0.921 & 0.865 & 0.884 & 0.817 & 0.897 & 0.823 & 0.926 & 0.868 & 0.855 & 0.782 \\ SAM-H &  
scenario of annotating polyp data and found that the time ratio between bounding box annotation and rough pixel-level annotation is approximately 1:6 (which would be even higher if precise pixel-level annotation is done by professional annotators). Based on this calculation, for example, if bounding box annotation takes 1 s and pixel-level annotation takes 6 s, to achieve the training data results of 56% to 62% under fully supervised learning with 100 images, the annotation time ratio would be approximately 27% to 30%, saving 70% to 73% of the time cost. This demonstrates the effectiveness of our method in reducing annotation costs.