## 4 Experimental Results and Discussion
### Visualization of the Attention Gate  
We further provide an illustration through a visualization of our scAG to obtain a deeply intuitive understanding of its efficacy in addition to showing quantitative results that demonstrate \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dice Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline U-Net & 65.93 \(\pm\) 6.34 & 61.49 \(\pm\) 7.30 & 70.00 \(\pm\) 6.62 \\ U-Net + encoder CBAM [25] & 68.37 \(\pm\) 6.09 & 66.00 \(\pm\) 7.79 & 73.48 \(\pm\) 5.42 \\ U-Net + scAG & **71.72 \(\pm\) 6.81** & **69.81 \(\pm\) 8.97** & **74.21 \(\pm\) 4.56** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance on the CVC-ClinicDB dataset by using the self-attention encoder feature (best results marked bold). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dice Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline U-Net & 65.93 \(\pm\) 6.34 & 61.49 \(\pm\) 7.30 & 70.00 \(\pm\) 6.62 \\ U-Net + AG [17,18] & 67.57 \(\pm\) 6.47 & 64.03 \(\pm\) 7.13 & 71.68 \(\pm\) 5.96 \\ U-Net + GAU [21] & 68.59 \(\pm\) 6.31 & 65.75 \(\pm\) 8.24 & 72.64 \(\pm\) 4.36 \\ U-Net + scAG & **71.72 \(\pm\) 6.81** & **69.81 \(\pm\) 8.97** & **74.21 \(\pm\) 4.56** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance on the CVC-ClinicDB dataset using other attention gates (best results marked bold). Figure 7: Example results by using separate attention gate. the effectiveness of our proposed method. For convenience, we assume that the U-Net model has four levels, where the first level corresponds to the first encoder feature and the last decoder feature whereas the last level corresponds to the last encoder feature and the first decoder feature. For cAG, it is difficult to produce a direct, explicit picture of the channel attention map. Therefore, we take some of the attended channels that are emphasized by cAG to see how much semantic information they contain. In Figure 8, we depict the channel of the encoder feature map that is emphasized by cAG. At each level, the left-side columns show the attended channel that has more emphasis while the right-side columns illustrate the attended channel that is less emphasized by cAG. As we can see, the channel with greater emphasis contains more semantic information than the other channel. For example, in the brain tumor TCGA dataset, we select the attended channel emphasized by cAG at all four levels, where the left-side columns (c,e,g,i) depict the channels with more emphasis that have a higher attention value of nearly one, while the right-side columns (d,f,g,k) show channels with less emphasis that have a lower attention value of nearly 0. As we can see, channels #9, #1, #68, #92 contain more noticeable semantic information of the 'tumor' class while channels #7, #38, #55, #44 contain worse semantic information. The encoder features enhanced by sAG now include more semantic concepts and, thus, fusing with the decoder features becomes more effective. For the sAG, we focus on the spatial attention map at all four levels. Figure 9 shows the spatial attention map from level #1 to level #4, which is multiplied with the corresponding encoder feature maps for further calculation. As we can see for the spatial attention map at level #4, where the spatial attention map depicts the general, coarse-grained details of the salient region to have a low resolution. As we gradually go to a higher resolution, the spatial attention map becomes more specific and fine-grained to emphasize the salient region of the target object. For example, look at row (i) of the polyp image of the CVC-ClinicDB dataset, we draw the spatial attention map created by sAG at all  
Figure 8: Channel feature map emphasized by the Channel Attention Gate (cAG). four levels. At level #4, where the feature maps have the coarse spatial information, sAG can only focus on the general, coarse-grained salient region of polyp in the features.