## 4 Experiments
### Results
#### 4.3.4 Further Analysis  
**Different Visual Backbones. ** We also investigated the segmentation capabilities of the SAM model with a different Vision Transformer (ViT) for encoding image features, as shown in Table 3. Table 3 (first, second, and third rows) shows the predictions of the original SAM model without fine-tuning. It can be observed that, on the ETIS dataset, although SAM-H and SAM-L are more computationally complex, their performance is not as good as SAM-B. To fully explore the transfer learning capabilities of SAM on different ViTs, we conducted fine-tuning. Considering memory limitations, in this work, we only fine-tuned SAM-B and SAM-L. From Table 3 (fourth row and fifth row), it can be observed that, under weakly supervised strategies, SAM-B outperforms SAM-L on the Kvasir-SEG, ClinicDB, and ETIS datasets, while performing less effectively on ColonDB and CVC-300 compared to SAM-L. This indicates that the lightweight SAM-B demonstrates better learning capabilities compared to the higher-complexity SAM-L.  
To verify this point, we performed full supervision fine-tuning on SAM-B and SAM-L using ground-truth labels. As shown in Table 3 (sixth row and seventh row), under full supervision, the trends across datasets align with those observed under weak supervision. In general, although SAM-L entails significantly greater computational complexity, it does not surpass the lightweight SAM-B model on three out of five public datasets. This finding suggests that the lightweight SAM-B model is well-suited for adoption in medical image applications. Additionally, we observed that, under the same ViT, weak supervision performs only 0.3% lower in average mDice than full supervision on the Kvasir-SEG, ColonDB, and CVC-300 datasets. This result indicates that, while weakly supervised methods reduce dependence on precise annotations, they can still maintain a segmentation precision comparable to fully supervised methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**MEF**} & \multirow{2}{*}{**PLW**} & \multirow{2}{*}{**MPP**} & \multicolumn{2}{c}{**Kvasir-SEG**} & \multicolumn{2}{c}{**ClinicDB**} & \multicolumn{2}{c}{**ColonDB**} & \multicolumn{2}{c}{**CVC-300**} & \multicolumn{2}{c}{**ETIS**} \\ \cline{4-14}  & & & & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** & **mDice** & **mIoU** \\ \hline - & - & - & - & - & 0.892 & 0.832 & 0.884 & 0.812 & 0.856 & 0.777 & 0.922 & 0.862 & 0.873 & 0.794 \\ \(\surd\) & & & & 0.928 & 0.873 & 0.914 & 0.850 & 0.895 & 0.820 & 0.931 & 0.875 & 0.876 & 0.796 \\ \(\surd\) & \(\surd\) & & & 0.928 & 0.875 & 0.914 & 0.851 & 0.897 & 0.822 & 0.931 & 0.875 & 0.880 & 0.797 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 0.932 & 0.878 & 0.916 & 0.851 & 0.903 & 0.830 & 0.933 & 0.878 & 0.885 & 0.805 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & **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 2: Ablation study of WSPolyp-SAM’s pseudo-labels. The bold indicates the highest scores. **Pseudo-label generation vs. WSPolyp-SAM. ** We conducted a series of experiments (see Table 4) comparing our method, WSPolyp-SAM, directly with pseudo-label generation, aiming to evaluate whether our fine-tuning approach is superior to the direct adoption of pseudo-label generation. The experimental results from five test datasets indicate that the mDice and mIoU scores obtained through the pseudo-label generation strategy are between the original predictions of SAM and those after fine-tuning, and they are significantly lower than the results after fine-tuning. This indicates that, while the pseudo-label generation method can optimize the predictions of the original SAM to some extent, there is still a certain gap compared to the predictions after fine-tuning.