## 3 Proposed Method
### Channel Attention Gate (cAG)  
Although the low-level encoder features are rich in detailed spatial information, they still lack semantic information. Due to this large gap in semantic concepts, a naive fusion of low-level encoder and high-level decoder features adversely affects the prediction procedure. A natural way to overcome this problem is to include more semantic concepts into low-level features, thus fusing becomes more effective. cAG is proposed in order to support 'what' is meaningful given an input feature. The channel attention map is constructed based on interdependencies between the channels of the convolutional features, which focus on meaningful discrimination of the features [28].  
The channel attention map is a combination of channel information from both the encoder and decoder features, as illustrated in Figure 3. The low-level encoder feature itself includes poor semantic information while the high-level decoder feature contains rich semantic information that can be used to support a low-level encoder feature to capture semantic dependencies. Therefore, improving the contextual information of the low-level encoder feature makes it easier to ensure effectual fusion. First, in each encoder and decoder feature, we squeeze spatial features by utilizing average pooling and max pooling simultaneously, as inspired by [25]. Second, we apply \(N\)\(1\times 1\) convolutions on the squeezed feature, where each \(1\times 1\) convolution has a role of capturing the dependencies of the channels to generate the squeeze channel attention map. In our method, \(N\) is assigned the value equal to \(1/16\) of \(C_{1}\), which is the number of channels of the encoder feature, to reduce the parameter overhead. Finally, we apply \(C_{1}\)\(1\times 1\) convolutions on the summarized encoder squeeze channel attention map and decoder squeeze channel attention map to construct the final attention map. The detailed operations are described below.  
We aggregate information along spatial dimension of each encoder and decoder feature map by using the pooling operation. \(F_{avg}^{c}\in\mathbb{R}^{C\times 1\times 1}\) and \(F_{max}^{c}\in\mathbb{R}^{C\times 1\times 1}\) denote the map generated from average pooling and max pooling, respectively. Those generated maps are then forwarded to the shared \(N\)\(1\times 1\) convolutions to produce a squeeze channel attention map. After the shared \(N\)\(1\times 1\) convolution operation, we combine the output attention by applying element-wise summation. \(M_{c}^{e}(F_{e})\) and \(M_{c}^{d}(F_{d})\) denote the generated squeeze channel attention map of the encoder and decoder features, respectively. The summation of \(M_{c}^{e}(F_{e})\) and \(M_{c}^{d}(F_{d})\) was forwarded to \(C_{1}\)\(1\times 1\) convolutions, and then a sigmoid function is applied in order to obtain the final channel attention map \(M_{c}(F_{e},F_{d})\). In short, the channel attention map is computed as:  
\[M_{c}^{e}(F_{e}) =F_{N}^{1\times 1}(AvgPool(F_{e}))+F_{N}^{1\times 1}(MaxPool(F_{e})) \tag{5}\] \[=F_{N}^{1\times 1}((F_{e})_{avg}^{c})+F_{N}^{1\times 1}((F_{e})_{ max}^{c})\] \[M_{c}^{d}(F_{d}) =F_{N}^{1\times 1}(AvgPool(F_{d}))+F_{N}^{1\times 1}(MaxPool(F_{d}))\] (6) \[=F_{N}^{1\times 1}((F_{d})_{avg}^{c})+F_{N}^{1\times 1}((F_{d})_{ max}^{c}) \tag{7}\]