Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention

Considering the problems of breast tumor size and shape change, blurred boundary and severe class imbalance between foreground and background, a multi-scale residual dual-domain attention fusion network is proposed. In this network, multi-scale residual blocks composed of multi-scale convolution are used as the basic building modules. Multi-scale residual block improves the network's ability to recognize targets of different sizes and the model's robustness by extracting multi-scale features and optimizing gradient propagation. Meanwhile, the dual-domain attention units are integrated into the network to improve the ability of edge recognition and boundary preservation. The hybrid loss function with adaptive weight is proposed, it can improve the optimization direction of the network, alleviate the influence of the extreme imbalance of positive and negative samples. The experimental results show that the average Dice value of the method proposed in this paper reaches 0.806 3, which is 5.3% higher than that of U-shaped Network (UNet), and the number of parameters is reduced by 73.36%, which has better segmentation performance.