A No-Math Primer on the Principles of Machine Learning for Radiologists.

Journal: Seminars in ultrasound, CT, and MR
Published Date:

Abstract

Machine learning is becoming increasingly important in both research and clinical applications in radiology due to recent technological developments, particularly in deep learning. As these technologies are translated toward clinical practice, there is a need for radiologists and radiology trainees to understand the basic principles behind them. This primer provides an accessible introduction to the vocabulary and concepts that are central to machine learning and relevant to the radiologist.

Authors

  • Matthew D Lee
    Department of Radiology, NYU Grossman School of Medicine, New York, NY.
  • Mohammed Elsayed
    Department of Radiology, NYU Grossman School of Medicine, New York, NY.
  • Sumit Chopra
    Imagen Technologies, New York, NY 10012.
  • Yvonne W Lui
    Center for Advanced Imaging Innovation and Research (CAI2R), School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA; Bernard and Irene Schwartz Center for Biomedical Imaging, School of Medicine, New York University, 660 First Avenue, New York, NY 10016, USA.