Deep learning with noisy labels in medical prediction problems: a scoping review.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jun 20, 2024

Abstract

OBJECTIVES: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included.

Authors

  • Yishu Wei
    Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States.
  • Yu Deng
    National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, People's Republic of China.
  • Cong Sun
    School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
  • Mingquan Lin
    Department of Population Health Sciences, Weill Cornell Medicine, New York, USA.
  • Hongmei Jiang
    Department of Statistics and Data Science, Northwestern University, Evanston, IL 60208, United States.
  • Yifan Peng
    Department of Population Health Sciences, Weill Cornell Medicine, New York, USA.

Keywords

External Resources

Popular Topics
Recent Journals