## 5 Ablation Experiment  
For this BMFA-Net model, we conducted four sets of ablation experiments. The experiments utilized Res2 Net + RA + PPD as the foundational framework and tested three modules, including the dilation selection of dilated convolutions, the EACA module, the multi-level feature aggregation mechanism, and the boundary constraint reverse attention module. We provided a comprehensive assessment of the efficacy of each component. The parameters and datasets used in these ablation experiments were aligned with the model's settings, ensuring the robustness of comparative analyses. Quantitative evaluations were performed using the mean Dice metric, and the impact of each module on the segmentation performance was demonstrated on the CVC-300 and CVC-ColonDB datasets. **The dilation selection of dilated convolutions:** Due to the traditional computation of atrous convolution resembling a checkerboard pattern, it can lead to the grid effect issue. Additionally, employing large dilation rates in convolutions may yield favorable segmentation results for larger objects but prove less friendly for smaller targets, like colorectal polyps. Therefore, the selection of the dilation rate is crucial. In our experiments, we tried several different dilation rate settings, and the results are presented in Table 6. The experimentation confirmed that a larger dilation rate was not necessarily better. In the case of small medical imaging targets like polyps, a larger receptive field was not required. Hence, following the principle that the dilation values in superimposed convolutions should not have a common divisor greater than one and should be designed in a zigzag manner, we conducted repeated experiments. The best segmentation accuracy was achieved when the dilation values were set to 1, 2, and 3. At this point, the model could better learn the multi-scale contextual information and prevent the grid effect from occurring. **Ablation experiments of three key modules:** The experimental results are shown in Table 7, clearly illustrating the influence of each module on the experiments. The efficient atrous convolution attention (EACA) module improved the model's segmentation accuracy by 3.2-6.3%. The experiment showed that the EACA module could not only explore a large amount of contextual information from multiple sensory domains to better apply the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dilation** & \multicolumn{2}{c}{**Mean Dice**} \\
**Setting** & **CVC-300** & **CVC-ColonDB** \\ \hline
6, 12, 18 & 0.878 & 0.710 \\
2, 4, 8 & 0.893 & 0.729 \\
2, 3, 5 & 0.915 & 0.813 \\
1, 2, 3 & **0.922** & **0.841** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative results for the different dilations. Figure 7: Visualization results with the current models. semantic information to the network but also capture the significant dependence of objects by using the attention mechanism, thus increasing the diversity of the feature extraction and reducing the computational amount. The EACA module can be plug and play, improving the accuracy of network segmentation. The multi-level feature aggregation (MFA) mechanism resulted in a 1.5-2.8% improvement. These findings highlight the complementary role of rich detail information from low-level features in enhancing the semantic information of high-level features. The inclusion of detailed features from the boundary constraint reverse attention module (BCRA) boosted the segmentation accuracy by 0.3-3.6%, and the experimental results confirmed the edge supervision in the superficial layer, using the position information extracted from the deep layer as the bilateral guidance, which could inhibit the non-polyp boundary features while enhancing the polyp boundary features, thus improving the segmentation accuracy. As shown in Figure 8, after removing the EACA module, the segmentation effect was significantly reduced, which indicates that the EACA module was the key module of BMFA-Net and played a positive role in achieving better segmentation performance.