Medical image segmentation model based on double attention and multiple expansion convolution fusion

The invention relates to a medical image segmentation model based on double attention and multiple expansion convolution fusion. The medical image segmentation model comprises a deep residual network, a double expansion attention network, a pyramid expansion module, a region generation network and a segmentation module. According to the method, the model pays more attention to the target region by improving and optimizing an attention module and the like, and the segmentation precision of the model on the region of interest and the boundary is improved; the problem that an existing target detection model tends to segment and classify instances of a target area, but edge precision segmentation is not high is solved, experiments prove that the segmentation result of a backbone network is well kept, and the segmentation result of a focus area is obviously superior to that of the prior art. 本申请涉及一种基于双注意力与多重扩张卷积融合的医学图像分割模型，包括：深度残差网络、双重扩张注意力网络、金字塔扩充模块、区域生成网络和分割模块；本申请通过改进优化注意力等模块来使模型更加关注目标区域，提高模型对感兴趣区域及边界的分割精度；解决了现有