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DGANet: A Dual Global Attention Neural Network for Breast Lesion Detection in Ultrasound Images

  • Hui Meng
    Affiliations
    School of Intelligent Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 1 Sub-lane Xiangshan, Hangzhou 310024, China
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  • Xuefeng Liu
    Affiliations
    State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Haidian District, Beijing, China
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  • Jianwei Niu
    Correspondence
    Address correspondence to: Jianwei Niu, State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China.
    Affiliations
    State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Haidian District, Beijing, China
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  • Yong Wang
    Affiliations
    Department of Diagnostic Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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  • Jintang Liao
    Affiliations
    Department of Ultrasound, Xiangya Hospital of Central South University, Changsha, China
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  • Qingfeng Li
    Affiliations
    Research Center of Big Data and Computational Intelligence, Hangzhou Innovation Institute of Beihang University, Hangzhou, China
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  • Chen Chen
    Affiliations
    State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Haidian District, Beijing, China
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      Abstract

      Deep learning-based breast lesion detection in ultrasound images has demonstrated great potential to provide objective suggestions for radiologists and improve their accuracy in diagnosing breast diseases. However, the lack of an effective feature enhancement approach limits the performance of deep learning models. Therefore, in this study, we propose a novel dual global attention neural network (DGANet) to improve the accuracy of breast lesion detection in ultrasound images. Specifically, we designed a bilateral spatial attention module and a global channel attention module to enhance features in spatial and channel dimensions, respectively. The bilateral spatial attention module enhances features by capturing supporting information in regions neighboring breast lesions and reducing integration of noise signal. The global channel attention module enhances features of important channels by weighted calculation, where the weights are decided by the learned interdependencies among all channels. To verify the performance of the DGANet, we conduct breast lesion detection experiments on our collected data set of 7040 ultrasound images and a public data set of breast ultrasound images. YOLOv3, RetinaNet, Faster R-CNN, YOLOv5, and YOLOX are used as comparison models. The results indicate that DGANet outperforms the comparison methods by 0.2%–5.9% in total mean average precision.

      Key Words

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