A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images.

Journal: JAMA network open
Published Date: Aug 2, 2021

Abstract

IMPORTANCE: Breast cancer screening is among the most common radiological tasks, with more than 39 million examinations performed each year. While it has been among the most studied medical imaging applications of artificial intelligence, the development and evaluation of algorithms are hindered by the lack of well-annotated, large-scale publicly available data sets.

Authors

  • Mateusz Buda
    Department of Radiology, Duke University School of Medicine, Durham, NC, USA; School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden. Electronic address: buda@kth.se.
  • Ashirbani Saha
    Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA. ashirbani.saha@duke.edu.
  • Ruth Walsh
    Department of Radiology, Duke University Medical Center, Durham, North Carolina.
  • Sujata Ghate
    Department of Radiology, Duke University Medical Center, Durham, North Carolina.
  • Nianyi Li
  • Albert Swiecicki
    Department of Electrical and Computer Engineering, Duke University, Durham, USA.
  • Joseph Y Lo
    Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina.
  • Maciej A Mazurowski
    Department of Radiology, Duke University School of Medicine, 2424 Erwin Road, Suite 302, Durham, NC, 27705, USA.

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