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中华医学超声杂志(电子版) ›› 2018, Vol. 15 ›› Issue (10) : 779 -782. doi: 10.3877/cma.j.issn.1672-6448.2018.10.010

所属专题: 乳腺超声 文献

浅表器官超声影像学

自动识别技术在乳腺结节超声图像良恶性分类中的可行性研究
卜云芸1, 卢树强2, 庞浩2, 罗昶2, 钱林学1,()   
  1. 1. 100050 首都医科大学附属北京友谊医院
    2. 518000 清影医疗(深圳)科技有限公司
  • 收稿日期:2018-07-01 出版日期:2018-10-01
  • 通信作者: 钱林学

Study on the feasibility of automatic recognition technology based on depth learning for classification of benign and malignant breast nodules in ultrasound images

Yunyun Bu1, Shuqiang Lu2, Hao Pang2   

  • Received:2018-07-01 Published:2018-10-01
引用本文:

卜云芸, 卢树强, 庞浩, 罗昶, 钱林学. 自动识别技术在乳腺结节超声图像良恶性分类中的可行性研究[J]. 中华医学超声杂志(电子版), 2018, 15(10): 779-782.

Yunyun Bu, Shuqiang Lu, Hao Pang. Study on the feasibility of automatic recognition technology based on depth learning for classification of benign and malignant breast nodules in ultrasound images[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2018, 15(10): 779-782.

图1~3 完整的乳腺结节检测路径。图1为图像预处理;图2为骨干网;图3为用于结节检测的特征金字塔部分
图4,5 乳腺结节检测输出。图4为良性结节;图5为恶性结节
表1 USNet模型对乳腺超声结节的目标检测结果
表2 USNet模型对乳腺超声结节分类结果
图6 USNet模型对乳腺超声结节分类的受试者工作特征曲线
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