# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 3 Proposed Methods
### Efficient Channel Attention Module  
In the process of extracting image features from neural networks, it is inevitable to encounter situations where some feature layers have a strong effect on classification or detection results, while others have relatively weak effects. An attention mechanism is a technology that simulates human physiological perception. When humans observe images, the visual system selects and focuses on analyzing important parts of the image. Drawing on this idea, an attention mechanism applies channel weights to feature maps and adaptively assigns different weights to different feature maps based on specific tasks, highlighting the feature maps that have a greater impact on the results and ignoring redundant information that is irrelevant to the results. In recent years, the attention mechanism has become an important component of convolutional neural networks, achieving significant performance improvements in computer vision tasks. The SE attention [34] mechanism can adaptively recalibrate channel feature responses by explicitly modeling the interdependence between channels. ECA improves SE attention by better learning interdependencies between channels and effectively increasing performance with very few additional parameters. Specifically, ECA replaces the MLP in the excitation step of the SE block with 1D convolution and proposes a local cross-channel interaction strategy to avoid information loss. Additionally, ECA introduces an adaptive method for selecting the size of the 1D convolutional kernel, which automatically selects the optimal receptive field size based on the size and number of the current feature map. This allows ECA to achieve significant performance gains while maintaining high dimensionality and implementing cross-channel interaction, all with minimal added parameters. Figure 7 shows the architecture of the ECA. First, global pooling is performed on features from each channel without dimensionality reduction. Then, using a 1D convolutional kernel with a receptive field of \(k\), each channel and its \(k\) nearest neighbor channels interact across channels, followed by allocating a new normalized weight to each channel via a Sigmoid activation function, and applying it to the previous feature map. Figure 6: The structure of DenseASPP. Figure 7: Efficient channel attention module. The ECA module intends to capture local cross-channel interactions appropriately, so it utilizes a matrix \(W_{k}\) to learn channel attention, and it involves \(k\times C\) parameters and avoids complete independence among different groups. Consider interactions between \(y_{i}\) and its \(k\) neighbors via sharing the same learning parameters across all channels, as shown in Equation (5):  
\[w_{i}=\sigma(\sum_{j=1}^{k}w^{i}y^{j}_{i}),y^{j}_{i}\in\Omega_{i}^{k} \tag{5}\]  
where \(\Omega_{i}^{k}\) is the set of \(k\) adjacent channels of \(y_{i}\). This approach can be implemented quickly with a 1D convolution of kernel size \(k\), as shown in Equation (6). \[w=\sigma(CID_{k}(y)) \tag{6}\]  
where \(CID\) indicates 1D convolution. To determine the weights of the generated channels, it is necessary to first determine the coverage of the interactions, i.e., the convolution kernel is \(k\), and \(k\) is adaptively determined by mapping the channel dimensions \(C\), as shown in Equation (7). \[k=\psi(C)=\left|\frac{\log_{2}(C)+b}{\gamma}\right|_{odd} \tag{7}\]  
where \(|t|_{odd}\) denotes the odd number to which \(t\) is closest. In all our experiments, we set \(\gamma\) and \(b\) to 2 and 1, respectively. By mapping \(\psi\), using nonlinear mapping, the range of interactions is larger for the higher-dimensional channels and smaller for the lower-dimensional channels. The ECA module provides a method to avoid a dimensional reduction in channel operations, which enables easy information interaction between channels and brings significant performance improvements with almost no extra computational cost.