Crowdsourcing human-based computation for medical image analysis: A systematic literature review
2020
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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-ba...sed 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.
Ključne reči:
medical image analysis / human-based computation / crowdsourcingIzvor:
Health Informatics Journal, 2020, 26, 4, 2446-2469Izdavač:
- SAGE Publications Ltd
Finansiranje / projekti:
- The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We acknowledge the Ministerio de Economía, Industria y Competitividad (MINECO), the Agencia Estatal de Investigación (AEI),
DOI: 10.1177/1460458220907435
ISSN: 1460-4582
PubMed: 32141371
WoS: 000523798700001
Scopus: 2-s2.0-85081606424
Institucija/grupa
Fakultet organizacionih naukaTY - JOUR AU - Petrović, Nataša AU - Moyà-Alcover, G. AU - Varona, J. AU - Jaume-i-Capó, A. PY - 2020 UR - https://rfos.fon.bg.ac.rs/handle/123456789/2098 AB - 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. PB - SAGE Publications Ltd T2 - Health Informatics Journal T1 - Crowdsourcing human-based computation for medical image analysis: A systematic literature review EP - 2469 IS - 4 SP - 2446 VL - 26 DO - 10.1177/1460458220907435 UR - conv_3606 ER -
@article{ author = "Petrović, Nataša and Moyà-Alcover, G. and Varona, J. and Jaume-i-Capó, A.", year = "2020", abstract = "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.", publisher = "SAGE Publications Ltd", journal = "Health Informatics Journal", title = "Crowdsourcing human-based computation for medical image analysis: A systematic literature review", pages = "2469-2446", number = "4", volume = "26", doi = "10.1177/1460458220907435", url = "conv_3606" }
Petrović, N., Moyà-Alcover, G., Varona, J.,& Jaume-i-Capó, A.. (2020). Crowdsourcing human-based computation for medical image analysis: A systematic literature review. in Health Informatics Journal SAGE Publications Ltd., 26(4), 2446-2469. https://doi.org/10.1177/1460458220907435 conv_3606
Petrović N, Moyà-Alcover G, Varona J, Jaume-i-Capó A. Crowdsourcing human-based computation for medical image analysis: A systematic literature review. in Health Informatics Journal. 2020;26(4):2446-2469. doi:10.1177/1460458220907435 conv_3606 .
Petrović, Nataša, Moyà-Alcover, G., Varona, J., Jaume-i-Capó, A., "Crowdsourcing human-based computation for medical image analysis: A systematic literature review" in Health Informatics Journal, 26, no. 4 (2020):2446-2469, https://doi.org/10.1177/1460458220907435 ., conv_3606 .