  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.
