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A Survey of Crowdsourcing in Medical Image Analysis

By Silas N Ørting, Andrew Doyle, Matthias Hirth, Christopher R Madan, Dom Panagiotis Mavridis, Helen Spiers, Veronika Cheplygina, Arno van Hilten and Oana Inel

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 a technique that is well established in a number of disciplines, including astronomy, ecology and meteorology 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 and challenges and provide recommendations to researchers implementing crowdsourcing for medical imaging tasks. Finally, we discuss future opportunities for development within this emerging domain

Publisher: 'Human Computation Institute'
Year: 2020
DOI identifier: 10.15346/hc.v7i1
OAI identifier: oai:nottingham-repository.worktribe.com:4905179
Provided by: Repository@Nottingham

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