## 3 Proposed Method
### Overview and Motivation  
The core idea of U-Net is to gradually fuse high semantic but coarse spatial decoder feature maps with the corresponding low semantic but fine spatial encoder feature maps. It provides location information from the encoder path to the decoder path in an attempt to recover the details of the spatial information that are lost by the max pooling operation.  
However, the working mechanism for U-Net still exhibits limitations and needs further investigation. First, multi-scale skip connections tend to use unnecessary information and computational sources where likable low-level encoder features are repeatedly used at multiple scales. When considering that low-level encoder features have rich spatial details, if the fusion mechanism can concentrate on the salient spatial region of the encoder feature, the network can recover fined-grained detail from the semantic results. Second, contextual information from the encoder feature is insufficient, leading to poor performance for pixel-wise recognition when concatenating the corresponding high-level decoder features. Because there are different semantic gaps between low-level encoder feature and the corresponding high-level decoder features, concatenating the two incompatible features will adversely affect the prediction procedures. The fusion between the encoder and decoder features could be more effective by increasing more semantic information into the low-level encoder features.  
Inspired through the above observations, we propose a novel spatial-channel attention gate (scAG) that aims to overcome the drawbacks of the working mechanism of U-Net. Our proposed model consists of four modules: encoder module, decoder module, fusion module and prediction module as shown in Figure 1. The input image is directly fed into the encoder module. The decoder features are generated by multiplying the encoder features with spatial-channel attention gate (scAG) and then concatenating with the corresponding decoder features. All of the decoder features are fused and then put into prediction module to predict abnormal tissue. scAG can adaptively boost location information and semantic information in the naive skip connection, leading to better segmentation results. The proposed scAG includes two types of attention modules that address the following aspects. (1) To guide the model to focus more on the spatial, detailed structure of an important region, we propose the spatial attention gate (sAG), and (2) to increase the contextual information into the low-level encoder feature, decreasing the semantic gap between the encoder and decoder features, we suggest the channel attention gate (cAG).  
Our proposed scAG stands in contrast to plain skip connections that lead to a combination of incompatible feature maps. Instead of a direct feed-forward of the low-level encoder feature to the corresponding high-level decoder feature, we propose scAG to be integrated into the skip connection, as illustrated in Figure 1. The scAG can obtain information from both the encoder and decoder feature maps to generate attention values that will be multiplied with encoder features to refine them before fusing with the corresponding decoder features.  
Given the features from the encoder and the corresponding features from the decoder, which have sizes of \(F_{e}\in\mathbb{R}^{C_{1}\times H\times W}\) and \(F_{d}\in\mathbb{R}^{C_{2}\times H\times W}\), respectively, where \(C_{1}\), \(C_{2}\) denote the number of channels and  
Figure 1: Proposed U-Net model using Spatial-Channel Attention Gate and Deep supervision.  
Figure 3: Diagram of the Channel Attention Gate (cAG).  
Figure 2: Diagram of the Spatial Attention Gate (sAG).