A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.

Abstract

Authors

• Huanhuan Liu
  Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China.
• Yanhong Chen
  Department of Cardiology, Wuhan Asia Heart Hospital, Wuhan, Hubei, China.
• Yuzhen Zhang
• Lijun Wang
  Department of Stomatology, The Third Medical Center Chinese PLA General Hospital Beijing China.
• Ran Luo
  Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China.
• Haoting Wu
  Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
• Chenqing Wu
  Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Joint Laboratory of Clinical Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
• Huiling Zhang
  Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Center for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
• Weixiong Tan
  Beijing Infervision Technology Co. Ltd., Beijing, 100025, China.
• Hongkun Yin
  Beijing Infervision Technology Co. Ltd., Beijing, 100025, China.
• Dengbin Wang
  Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China. wangdengbin@xinhuamed.com.cn.

Keywords

External Resources