دورية أكاديمية
Boosting Wisdom of the Crowd for Medical Image Annotation Using Training Performance and Task Features
| العنوان: | Boosting Wisdom of the Crowd for Medical Image Annotation Using Training Performance and Task Features |
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| اللغة: | English |
| المؤلفون: | Eeshan Hasan (ORCID |
| المصدر: | Cognitive Research: Principles and Implications. 2024 9. |
| الإتاحة: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 21 |
| تاريخ النشر: | 2024 |
| Sponsoring Agency: | National Science Foundation (NSF), Division of Social and Economic Sciences (SES) |
| Contract Number: | 2242962 1846764 |
| نوع الوثيقة: | Journal Articles Reports - Research |
| Descriptors: | Algorithms, Human Body, Classification, Knowledge Level, Group Behavior, Medicine, Illustrations, Visual Aids, Documentation, Training, Artificial Intelligence |
| DOI: | 10.1186/s41235-024-00558-6 |
| تدمد: | 2365-7464 |
| مستخلص: | A crucial bottleneck in medical artificial intelligence (AI) is high-quality labeled medical datasets. In this paper, we test a large variety of wisdom of the crowd algorithms to label medical images that were initially classified by individuals recruited through an app-based platform. Individuals classified skin lesions from the International Skin Lesion Challenge 2018 into 7 different categories. There was a large dispersion in the geographical location, experience, training, and performance of the recruited individuals. We tested several wisdom of the crowd algorithms of varying complexity from a simple unweighted average to more complex Bayesian models that account for individual patterns of errors. Using a switchboard analysis, we observe that the best-performing algorithms rely on selecting top performers, weighting decisions by training accuracy, and take into account the task environment. These algorithms far exceed expert performance. We conclude by discussing the implications of these approaches for the development of medical AI. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| رقم الأكسشن: | EJ1425020 |
| قاعدة البيانات: | ERIC |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 2365-7464 |
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| DOI: | 10.1186/s41235-024-00558-6 |