# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 4 Discussion
### Effectiveness of BPM Combinations  
In BPM, explicit boundary information is obtained by combining the low-level feature \(E_{1}^{(1)}\), which has rich boundary information, and the highest-level feature \(D_{4}^{(1)}\) of the first decoder. As presented in Table 8, experiments were conducted to investigate the effects of different level features of the decoder (\(D_{1-4}^{(1)}\)). Based on mDice and mIoU, the combination with \(D_{4}^{(1)}\), which is the highest-level representation, yielded much better performance on the CVC-ClinicDB and CVC-T datasets than \(D_{2}^{(1)}\) and \(D_{3}^{(1)}\), respectively. However, on the Kvasir and ColonDB datasets, the combination with \(D_{2}^{(1)}\) and \(D_{3}^{(1)}\) performed best, respectively. In MAE, we observe that the combination with \(D_{4}^{(1)}\) outperformed all the datasets. This experiment suggests that \(D_{4}^{(1)}\), which is the representation with the most abstraction, extracts clean boundary information by removing the background noise of \(E_{1}^{(1)}\). Moreover, as in the previous methods [20,23], an experiment was performed to extract and propagate  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Dataset** & **Model** & **mDice** & **mIoU** & \(\mathbf{S_{m}}\) & \(\mathbf{E_{m}}\) & **MAE** \\ \hline \multirow{4}{*}{CVC-ClinicDB} & Base & 0.898 & 0.850 & 0.933 & 0.967 & 0.010 \\  & Base + CMM & 0.926 & 0.882 & 0.945 & 0.981 & 0.008 \\  & Base + BPM & 0.911 & 0.863 & 0.940 & 0.972 & 0.008 \\  & Base + CMM + BPM & **0.933** & **0.891** & **0.956** & **0.985** & **0.007** \\ \hline \multirow{4}{*}{Kvasir} & Base & 0.882 & 0.831 & 0.906 & 0.945 & 0.032 \\  & Base + CMM & 0.897 & 0.847 & 0.915 & 0.949 & 0.029 \\  & Base + BPM & 0.890 & 0.836 & 0.912 & 0.950 & 0.034 \\  & Base + CMM + BPM & **0.901** & **0.852** & **0.919** & **0.951** & **0.027** \\ \hline \multirow{4}{*}{ColonDB} & Base & 0.676 & 0.615 & 0.806 & 0.810 & 0.043 \\  & Base + CMM & 0.720 & 0.652 & 0.822 & 0.855 & 0.040 \\  & Base + BPM & 0.671 & 0.609 & 0.803 & 0.813 & 0.044 \\  & Base + CMM + BPM & **0.754** & **0.689** & **0.849** & **0.897** & **0.037** \\ \hline \multirow{4}{*}{ETIS} & Base & 0.628 & 0.567 & 0.799 & 0.769 & 0.028 \\  & Base + CMM & 0.675 & 0.609 & 0.832 & 0.872 & 0.021 \\  & Base + BPM & 0.664 & 0.597 & 0.816 & 0.810 & 0.018 \\  & Base + CMM + BPM & **0.711** & **0.648** & **0.844** & **0.887** & **0.015** \\ \hline \multirow{4}{*}{CVC-T} & Base & 0.830 & 0.752 & 0.890 & 0.931 & 0.015 \\  & Base + CMM & 0.887 & 0.819 & 0.936 & 0.985 & 0.008 \\ \cline{1-1}  & Base + BPM & 0.859 & 0.784 & 0.915 & 0.961 & 0.009 \\ \cline{1-1}  & Base + CMM + BPM & **0.906** & **0.843** & **0.945** & **0.988** & **0.006** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation studies for the combination of modules.  
boundary information using an independent boundary decoder (row 4). We observe that the independent boundary decoder performs similarly well on CVC-ClincDB and Kvasir datasets, while its usage leads to a drop in the generalization performance (i.e., three unseen datasets). This suggests that using the independent boundary decoder with more parameters may better represent the distribution of the training dataset (i.e., CVC-ClinicDB, Kvasir) but is not good for generalization.