# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 3 Results
### Further Experiments
#### 3.4.2 Individual Learning  
In the experiments, we trained a total dataset that merged CVC-ClinicDB and Kvasir datasets to obtain the leverage effect of a large distribution. We compared the results of training using CVC-ClinicDB and Kvasir as training datasets to investigate whether the benefits of the total dataset help to improve performance. As listed in Table 5, we observed that using the total dataset training strategy outperformed individual dataset training on three datasets (i.e., CVC-ClinicDB, ETIS, and CVC-T). The mean dice scores were 1.4-5.6% higher than when individually trained. However, using the total dataset training strategy slightly worsens the performance on the Kvasir dataset, whereas it performed similarly on the ColonDB dataset. This suggests that the data distribution of the CVC-ClinicDB dataset included in the total dataset is different from that of the Kvasir dataset. Therefore, noise was added to the distribution of the Kvasir dataset, resulting in a slight performance degradation. Nevertheless, we note that using a joint dataset training strategy is robust in terms of the generalization performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **\#Decoder (d)** & **\#Params** & **mDice** & **mIoU** & \(S_{m}\) & \(E_{m}\) & **MAE** \\ \hline \multirow{6}{*}{CVC-ClinicDB} & 1 & 28.43 M & 0.919 & 0.875 & 0.944 & 0.978 & 0.0072 \\  & 2 & 31.10 M & **0.933** & **0.891** & **0.956** & **0.985** & **0.0066** \\  & 3 & 33.76 M & 0.925 & 0.882 & 0.945 & 0.981 & 0.0074 \\  & 4 & 36.42 M & 0.931 & 0.889 & 0.953 & 0.982 & 0.0068 \\  & 5 & 39.09 M & 0.921 & 0.878 & 0.950 & 0.979 & 0.0074 \\ \hline \multirow{6}{*}{Kvasir} & 1 & 28.43 M & 0.870 & 0.823 & 0.898 & 0.920 & 0.032 \\  & 2 & 31.10 M & **0.901** & **0.852** & **0.919** & 0.951 & **0.027** \\  & 3 & 33.76 M & 0.898 & 0.849 & **0.919** & 0.953 & 0.028 \\  & 4 & 36.42 M & 0.897 & 0.848 & 0.918 & **0.957** & 0.028 \\  & 5 & 39.09 M & **0.901** & 0.851 & **0.919** & 0.950 & **0.027** \\ \hline \multirow{6}{*}{ColonDB} & 1 & 28.43 M & 0.701 & 0.665 & 0.817 & 0.844 & 0.051 \\  & 2 & 31.10 M & 0.754 & 0.689 & 0.849 & **0.897** & 0.037 \\  & 3 & 33.76 M & **0.762** & **0.697** & **0.852** & 0.874 & 0.039 \\  & 4 & 36.42 M & 0.756 & 0.689 & 0.850 & 0.875 & **0.035** \\  & 5 & 39.09 M & 0.753 & 0.689 & 0.846 & 0.876 & 0.039 \\ \hline \multirow{6}{*}{ETIS} & 1 & 28.43 M & 0.677 & 0.621 & 0.831 & 0.880 & 0.0160 \\  & 2 & 31.10 M & **0.711** & 0.648 & **0.844** & 0.887 & **0.0151** \\  & 3 & 33.76 M & 0.708 & 0.644 & **0.844** & 0.878 & 0.0176 \\  & 4 & 36.42 M & **0.711** & **0.649** & **0.844** & **0.893** & 0.0164 \\  & 5 & 39.09 M & 0.694 & 0.633 & 0.836 & 0.874 & 0.0167 \\ \hline \multirow{6}{*}{CVC-T} & 1 & 28.43 M & 0.850 & 0.793 & 0.894 & 0.957 & 0.012 \\  & 2 & 31.10 M & **0.906** & **0.843** & **0.945** & **0.988** & **0.006** \\ \cline{1-1}  & 3 & 33.76 M & 0.892 & 0.826 & 0.935 & 0.987 & 0.007 \\ \cline{1-1}  & 4 & 36.42 M & 0.870 & 0.803 & 0.927 & 0.964 & 0.008 \\ \cline{1-1}  & 5 & 39.09 M & 0.888 & 0.822 & 0.936 & 0.977 & 0.009 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of COMMA performance based on the decoder structures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Model** & **mDice** & **mIoU** & \(S_{m}\) & \(E_{m}\) & **MAE** \\ \hline \multirow{6}{*}{CVC-ClinicDB} & PraNet & 0.899 & 0.853 & 0.937 & 0.979 & 0.009 \\  & COMMA \({}^{\dagger}\) & 0.919 & 0.877 & 0.948 & 0.984 & **0.007** \\  & **COMMA** & **0.933** & **0.891** & **0.956** & **0.985** & **0.007** \\ \hline \multirow{6}{*}{Kvasir} & PraNet & 0.897 & 0.844 & 0.915 & 0.948 & 0.030 \\  & COMMA \({}^{*}\) & **0.913** & **0.867** & **0.929** & **0.965** & **0.024** \\ \cline{1-1}  & **COMMA** & 0.901 & 0.852 & 0.919 & 0.951 & 0.027 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Leverage effectiveness in large samples. \(\dagger\) and \({}^{*}\) indicate that CVC-ClinicDB and Kvasir were used as training datasets, respectively.