Li, Yue
He, Zilong
Lu, Yao
Ma, Xiangyuan
Guo, Yanhui
Xie, Zheng
Qin, Genggeng
Xu, Weimin
Xu, Zeyuan
Chen, Weiguo
Chen, Haibin
Funding for this research was provided by:
Guangdong Province Key Laboratory of Computational Science (2018009)
Fundamental Research Funds for the Central Universities (19LGYJS63)
Science and Technology Planning Project of Guangdong Province (2015B020233002)
Guangzhou Science and Technology Creative Project (201604020003)
Clinical Research Startup Program of Southern Medical University by High-level University Construction Funding of Guangdong Provincial Department of Education (LC2016ZD018)
Clinical Research Program of Nanfang Hospital, Southern Medical University (2018CR040)
Natural Science Foundation of Guangdong Province, China (2019A1515011168)
National Key R&D Program of China (2016YFB0200602)
National Natural Science Foundation of China (11401601)
Science and Technology Innovative Project of Guangdong Province (2015B010110003)
Construction Project of Shanghai Key Laboratory of Molecular Imaging (18DZ2260400)
Article Title: Deep learning of mammary gland distribution for architectural distortion detection in digital breast tomosynthesis
Journal Title: Physics in Medicine & Biology
Article Type: paper
Copyright Information: © 2021 Institute of Physics and Engineering in Medicine
Publication dates
Date Received: 2019-11-26
Date Accepted: 2020-06-02
Online publication date: 2021-01-29