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在公开可用的胎儿超声数据集中发现道德偏见
Maria Chiara Fiorentino1, Sara Moccia2, Mariachiara Di Cosmo3
1Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy. m.c.fiorentino@staff.univpm.it.
NPJ digital medicine
|June 13, 2025
概括
公共可用的胎儿超声数据集在人口统计,临床条件和技术方面表现出偏差. 解决这些偏见对于产前诊断中的公平深度学习至关重要.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
背景情况:
- 深度学习 (DL) 越来越多地用于产前诊断.
- 公共胎儿超声波 (美国) 成像数据集对于训练DL算法至关重要.
- 鉴于潜在的医疗保健不平等,评估这些数据集的公平性至关重要.
研究的目的:
- 调查公开可用的胎儿超声波成像数据集中的偏差.
- 了解这些偏见对深度学习模型性能的影响.
- 提出减轻发现偏见的策略.
主要方法:
- 对现有的胎儿美国成像数据集的全面分析.
- 确定人口统计,临床状况和技术多样性问题.
- 对DL算法的公平性和性能潜在影响的评估.
主要成果:
- 美国胎儿数据集在人口代表性上显示出显著的偏差.
- 在描绘的临床条件中观察到有限的多样性.
- 跨数据集的超声波技术的变化是一个挑战.
结论:
- 数据集偏差可能会对DL模型的性能产生负面影响,并导致医疗保健差异.
- 促进数据包容性和完善DL策略至关重要.
- 增强数据集多样性将提高DL在胎儿美国分析的可靠性.
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