## 4 Experiments
### Results
#### 4.3.3 Ablation Study  
Our method includes a key component, which is pseudo-label generation based on SAM. It consists of three parts: multi-enhancement fusion (MEF), pixel-level weighting (PLW), and mask post-processing (MPP). In this section, we conduct comprehensive ablation experiments on five colonoscopy polyp datasets, with results shown in Table 2. The first row presents the results of directly using SAM to generate segmentation masks on the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \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 U-Net & 0.818 & 0.746 & 0.823 & 0.755 & 0.504 & 0.436 & 0.710 & 0.627 & 0.398 & 0.335 \\ U-Net++ & 0.821 & 0.744 & 0.794 & 0.729 & 0.482 & 0.408 & 0.707 & 0.624 & 0.401 & 0.344 \\ SFA & 0.723 & 0.611 & 0.700 & 0.607 & 0.456 & 0.337 & 0.467 & 0.329 & 0.297 & 0.271 \\ PraNet & 0.898 & 0.840 & 0.899 & 0.849 & 0.712 & 0.640 & 0.871 & 0.797 & 0.628 & 0.567 \\ C2 FNet & 0.886 & 0.831 & 0.919 & 0.872 & 0.724 & 0.650 & 0.874 & 0.801 & 0.699 & 0.624 \\ Polypy-PVT & 0.917 & 0.864 & **0.948** & **0.905** & 0.808 & 0.727 & 0.900 & 0.833 & 0.787 & 0.706 \\ HSNet & 0.926 & 0.877 & 0.937 & 0.887 & 0.810 & 0.735 & 0.903 & 0.839 & 0.808 & 0.734 \\ \hline SAM-B & 0.892 & 0.832 & 0.884 & 0.812 & 0.856 & 0.777 & 0.922 & 0.862 & 0.873 & 0.794 \\ WSPoly-SAM-B & **0.933** & **0.879** & 0.920 & 0.858 & **0.904** & **0.831** & **0.933** & **0.878** & **0.888** & **0.813** \\ \(\Delta\) & 0.041 & 0.047 & 0.036 & 0.046 & 0.048 & 0.054 & 0.011 & 0.016 & 0.015 & 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results of WSPolyp-SAM and SAM on Kvasir-SEG, ClinicDB, ColonDB, CVC-300 and ETIS datasets. Here, ’B’ represents the use of ViT-B, and ’\(\Delta\)’ represents the increase relative to SAM. The bold indicates the highest scores.  
Figure 4: Visualization results with different models.  
five datasets. As for the baseline, we used SAM to generate a segmentation mask for training images and fine-tuned SAM (second row) as the baseline without data augmentation. We then gradually added MEF (third row), PLW (fourth row), and MPP (fifth row) on top of the baseline, ultimately achieving the best evaluation scores.  
Furthermore, we performed visual analysis to evaluate the influence of each module on the quality of model predictions (see Figure 3). It can be observed that, in the baseline scenario, due to unclear boundaries between polyps and surrounding mucosa and low contrast between polyp foreground and background information, 'Baseline' exhibits issues such as unclear predicted edges and missed detections. After introducing 'MEF', the fusion of multiple enhancement results provided additional predictive information, significantly reducing instances of missed detections. Subsequently, with the introduction of 'PLW', assigning higher weights to reliable pixels further reduced the probability of error detection. Finally, with the inclusion of 'MPP', better noise reduction during prediction and improvement in edge clarity were achieved, further enhancing the segmentation results. In summary, our method enhances the detection coverage of polyp regions, reduces segmentation errors, and minimizes missed detections.