# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 2 Related Works
For the improvement of network modules, the current typical modules mainly include the inception module ([PERSON] et al., 2019), attention mechanism, multi-scale feature extraction module, dense connection module, and deep separable module. In neural networks, feature maps with larger receptive fields contain richer information and can better capture global information. Therefore, [PERSON] et al. (2019) designed a pyramid pooling module to acquire features at different scales and merge them through upsampling and concatenation. However, during the upsampling process, the network may struggle to capture small lesion regions, leading to inaccurate segmentation. [PERSON] et al. (2019) proposed using Atrous ([PERSON] et al., 2019) spatial pyramid pooling to capture multi-scale features. This structure consists of five parallel branches, each processing the input feature maps with different convolutional kernels and cascading them at the output. It can obtain a larger receptive field without increasing the parameters. Additionally, to fully utilize the information from different receptive fields, [PERSON] et al. (2019) proposed DenseASPP, which shares feature information through a cascading approach. This method differs from the parallel connection approach with atrous convolutions used in ASPP. By utilizing a dense connection of a set of atrous convolutions, it covers a larger range and obtains more multi-scale information without increasing the model size. In recent years, some researchers have introduced the DenseASPP module into their networks and achieved good results. For example, [PERSON] et al. (2019) trained a DenseASPP model to learn the location and probability maps of the pancreas. In the segmentation stage, a saliency-aware module that combines saliency maps and image context was introduced in DenseASPP. This method utilizes the prominent pancreas features in coarse segmentation and improves the accuracy in the fine segmentation stage. [PERSON] et al. (2019) proposed an automatic mandible segmentation method using a 3D fully convolutional neural network. They incorporated the DenseASPP module into the network to extract dense features at multiple scales. Attention gate (AG) modules were applied in each skip connection to reduce irrelevant background information and focus the network on the segmentation region. The proposed network achieved good segmentation performance and accurate automatic mandible segmentation. [PERSON] et al. (2019) collected information across scales through dense connections in the DenseASPP module, enriching multi-scale contextual features. They combined residual connections between encoder and decoder blocks to learn more robust features and achieve better predictions. [PERSON] et al. (2019) designed an efficient architecture named PSSAC that improves ASPP and DenseASPP by introducing exponentially increasing scales with a serially connected multiple parallel structure. They explicitly adjusted the receptive field of neurons to capture high-level features at an ultra-dense scale. This approach addressed the problem of semantic segmentation in large-scale variations and complex scenes. In some studies, attention mechanisms were introduced to enhance feature extraction by selectively attending to relevant features while ignoring irrelevant ones. [PERSON] et al. [28] proposed a network structure that combines spatial attention and channel attention, enhancing feature extraction without significantly increasing computational complexity or model parameters. [PERSON] et al. [29] combined channel attention and spatial attention mechanisms based on the UNet architecture and used a weighted cross-entropy loss function, achieving excellent results in skin lesion segmentation datasets. [PERSON] et al. [30] enhanced the resolution of skin lesion segmentation by combining spatial and channel attention mechanisms. The spatial attention pathway captures spatially related features of skin lesions, while the channel attention pathway selectively emphasizes discriminative features in the channel dimension.