Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (3): 818-824.DOI: 10.11772/j.issn.1001-9081.2021040948

• 2021 CCF Conference on Artificial Intelligence (CCFAI 2021) • Previous Articles    

Multiscale residual UNet based on attention mechanism to realize breast cancer lesion segmentation

Shengqin LUO1, Jinyi CHEN1, Hongjun LI1,2()   

  1. 1.School of Information Science and Technology,Nantong University,Nantong Jiangsu 226019,China
    2.State Key Laboratory for Novel Software Technology (Nanjing University),Nanjing Jiangsu 210023,China
  • Received:2021-06-04 Revised:2021-06-22 Accepted:2021-06-29 Online:2021-11-09 Published:2022-03-10
  • Contact: Hongjun LI
  • About author:LUO Shengqin, born in 1997, M. S. candidate. His research interests include deep learning, medical image processing.
    CHEN Jinyi, born in 1996, M. S. candidate. His research interests include deep learning, computer vision.
  • Supported by:
    National Natural Science Foundation of China(61976120);Jiangsu Industry University Research Cooperation Project(BY2021349);Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX21_3084);Science and Technology Program of Nantong(JC2021131);State Key Laboratory for Novel Software Technology at Nanjing University(KFKT2019B015)


罗圣钦1, 陈金怡1, 李洪均1,2()   

  1. 1.南通大学 信息科学技术学院,江苏 南通 226019
    2.计算机软件新技术国家重点实验室(南京大学),南京 210023
  • 通讯作者: 李洪均
  • 作者简介:罗圣钦(1997—),男,江苏盐城人,硕士研究生,CCF会员,主要研究方向:深度学习、医学图像处理
  • 基金资助:


Concerning the characteristics of breast cancer in Magnetic Resonance Imaging (MRI), such as different shapes and sizes, and fuzzy boundaries, an algorithm based on multiscale residual U Network (UNet) with attention mechanism was proposed in order to avoid error segmentation and improve segmentation accuracy. Firstly, the multiscale residual units were used to replace two adjacent convolution blocks in the down-sampling process of UNet, so that the network could pay more attention to the difference of shape and size. Then, in the up-sampling stage, layer-crossed attention was used to guide the network to focus on the key regions, avoiding the error segmentation of healthy tissues. Finally, in order to enhance the ability of representing the lesions, the atrous spatial pyramid pooling was introduced as a bridging module to the network. Compared with UNet, the proposed algorithm improved the Dice coefficient, Intersection over Union (IoU), SPecificity (SP) and ACCuracy (ACC) by 2.26, 2.11, 4.16 and 0.05 percentage points, respectively. The experimental results show that the algorithm can improve the segmentation accuracy of lesions and effectively reduce the false positive rate of imaging diagnosis.

Key words: breast cancer lesion segmentation, multiscale residual, attention mechanism, bridging module, false positive rate



关键词: 乳腺癌灶分割, 多尺度残差, 注意力机制, 桥接模块, 假阳性率

CLC Number: