LAUSR

Computer-assisted algorithms for the analysis of medical images require human interactions to achieve satisfying results. Human-based computation and crowdsourcing offer a solution to this problem. We performed a systematic literature review of studies on crowdsourcing human-based computation for medical image analysis based on the guidelines proposed by Kitchenham and Charters. We identified 43 studies relevant to the objective of this research. We determined three primary purposes and problems that crowdsourcing human-based computation systems can solve. We found that the users provided five information types. We compared systems that use pre-, post-evaluation and quality control methods to select and filter the user inputs. We analyzed the metrics used for the evaluation of the crowdsourcing human-based computation system performance. Finally, we identified the most popular crowdsourcing human-based computation platforms with their advantages and disadvantages.Crowdsourcing human-based computation systems can successfully solve medical image analysis problems. However, the application of crowdsourcing human-based computation systems in this research area is still limited and more studies should be conducted to obtain generalizable results. We provided guidelines to practitioners and researchers based on the results obtained in this research.