Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks.

Abstract

Authors

• Hongna Tan
  Department of Radiology, Henan Provincial People's Hospital, China.
• Qingxia Wu
  College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, Liaoning, China.
• Yaping Wu
  Department of Imaging, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
• Bingjie Zheng
  Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
• Bo Wang
  Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
• Yan Chen
  Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
• Lijuan Du
  Department of Radiology, Zhengzhou Central Hospital, Zhengzhou, China.
• Jing Zhou
• Fangfang Fu
  Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, Henan, China.
• Huihui Guo
  Department of Radiology, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, China.
• Cong Fu
  Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao, Nanjing, 210009, Jiangsu, China.
• Lun Ma
  Department of Radiology, Fuwai Central China Cardiovascular Hospital, Zhengzhou, China.
• Pei Dong
• Zhong Xue
  Shanghai United Imaging Intelligence Co Ltd., Shanghai, China.
• Dinggang Shen
  School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
• Meiyun Wang

Keywords

No keywords available for this article.

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