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Volume 45 Issue 5
May  2023
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LIU Xia, LÜ Zhiwei, LI Bo, WANG Bo, WANG Di. Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1774-1785. doi: 10.11999/JEIT220362
Citation: LIU Xia, LÜ Zhiwei, LI Bo, WANG Bo, WANG Di. Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1774-1785. doi: 10.11999/JEIT220362

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

doi: 10.11999/JEIT220362
Funds:  The National Natural Science Foundation of China (61172167), The Youth Science Foundation of Heilongjiang Province (QC2017076)
  • Received Date: 2022-03-31
  • Rev Recd Date: 2022-07-15
  • Available Online: 2022-07-26
  • Publish Date: 2023-05-10
  • 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 0.8063, 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.
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