# Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
## 2 Related Work  
Considering the limitations of traditional segmentation methods and the need for high accuracy in medical image segmentation, rapidly developing deep learning techniques have been widely used in the field of medical images, mainly due their its advantages in segmentation speed and accuracy, which can significantly reduce the time and help doctors diagnose diseases more efficiently. Fully Convolutional Networks (FCN) proposed by [PERSON] et al. [10] have become the mainstream framework in the field of image semantic segmentation, which is the pioneering work of the most successful and advanced deep learning technology. For the first time, FCN realizes the pixel-level semantic segmentation task that can accept inputs of any image size. It replaces the fully connected layer in the CNN model with a fully convolutional layer to achieve pixel-level dense prediction, uses deconvolution to upsample the feature map, and proposes skip layer connections to fully integrate global semantic information and local location information to achieve precise segmentation. However, the disadvantages of FCN are also obvious. First, the upsampling process is rough, resulting in serious loss of semantic information of feature maps, which seriously affects the segmentation accuracy. Second, skip connections fail to make full use of the context information and spatial location information of the image, resulting in low utilization of global information and local information. Deep convolutional neural network uses repeated convolution and pooling to increase the range of the receptive field and improve the ability of the feature to express the global information, but it will cause the problem of reduced resolution and loss of detailed information. Furthermore, the features acquired by convolution are spatially invariant, which hinders the segmentation task [11]. For the above problems, DeepLab v1, proposed by [PERSON] et al. [12], reduces the loss of detailed information through atrous convolution while increasing the receptive field and uses CRF [13] to improve the ability to obtain boundary details. It is based on VGGNet as the backbone network to obtain image semantic information, upsample the features, and finally use the CRF to obtain a more accurate segmentation effect. DeepLab v2 [14] is an improved method based on DeepLab v1. It uses ResNet-101 as the backbone network and add continuous atrous convolution to replace the original downsampling layer, which improves the resolution of the output layer while maintaining the same receptive field. Another contribution of DeepLab lies in employing the ASPP module to extract and fuse multi-scale information of feature layer information, effectively fuse local and global context information, and improve the segmentation accuracy of the model. In addition, pixel-level classification belongs to low-level semantic information, so it appears very vague in local details [15]. DeepLab v2 makes the position of the segmentation boundary more accurate with the help of a fully connected CRF. DeepLab v3 [16] continues to uses ResNet-101 as the backbone network. For multi-scale target segmentation, the ASPP module has been redesigned, which is composed of atrous convolution and BN layers with different sampling rates, and the modules are arranged in a serial or parallel manner to obtain larger receptive fields and obtain multi-scale information. In addition, DeepLab v3 removes CRF and the experimental result showed that it is better than DeepLab v1 and v2. DeepLab v3+ [17], further expands DeepLab v3, and adds a simple and effective decoding module to improve the segmentation efficiency of the model, especially in the performance of target boundary segmentation, with a greater degree of optimization. Besides, through further study of the Xception model, the maximum pooling is replaced by deep separable convolution, which is used in ASPP and the decoder network structure to obtain a faster and more robust encoder-decoder network.