A Survey of Crowdsourcing in Medical Image Analysis

Authors

  • Silas Nyboe Ørting University of Copenhagen http://orcid.org/0000-0002-3081-1547
  • Andrew Doyle McGill Centre for Integrative Neuroscience
  • Arno van Hilten Erasmus Medical Center
  • Matthias Hirth Technische Universität Ilmenau
  • Oana Inel Vrije Universiteit Amsterdam
  • Christopher R Madan University of Nottingham
  • Panagiotis Mavridis Delft University of Technology
  • Helen Spiers University of Oxford, Zooniverse
  • Veronika Cheplygina Eindhoven University of Technology

Keywords:

Medical Imaging, Crowdsourcing, Citizen Science, Machine Learning

Abstract

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

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Published

2020-12-01

How to Cite

Ørting, S. N., Doyle, A., van Hilten, A., Hirth, M., Inel, O., Madan, C. R., Mavridis, P., Spiers, H., & Cheplygina, V. (2020). A Survey of Crowdsourcing in Medical Image Analysis. Human Computation, 7(1), 1-26. Retrieved from https://hcjournal.org/index.php/jhc/article/view/111

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