深度学习模型如何在人群中泛化? 跨民族对COPD检测的概括
Silvia D Almeida1,2,3,4, Tobias Norajitra5,6, Carsten T Lüth7,8
1Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany. silvia.diasalmeida@dkfz-heidelberg.de.
Insights into imaging
|August 7, 2024
概括
自主监督学习 (SSL) 和均衡数据集显著改善了在胸部CT扫描上慢性阻塞性肺病 (COPD) 的检测. 这种方法提高了模型的性能,并减少了对公平的人工智能医疗保健的种族偏见.
科学领域:
- 医疗成像中的人工智能
- 肺部病理学 肺部病理学
- 放射学 放射学是一门学科.
背景情况:
- 在胸部CT扫描上检测慢性阻塞性肺病 (COPD) 的深度学习模型可能会在不同族群中表现出差异性差异.
- 评估和减轻人工智能模型中的潜在偏差对于公平的医疗保健服务至关重要.
研究的目的:
- 评估深度学习模型在胸部CT扫描上检测COPD的性能和种族偏差.
- 为了比较监督学习 (SL) 和自我监督学习 (SSL) 策略.
- 调查培训数据集组成 (种族特异性与平衡) 对模型性能和偏差的影响.
主要方法:
- 胸部CT扫描和7549名患者的临床数据的回顾性分析 (5240名非西班牙裔白人,2309名非裔美国人).
- 模型使用不同的策略进行训练:人口特异性 (仅NHW,仅AA),平衡 (NHW + AA) 和组合数据集.
- 监督学习 (SL) 与自主监督学习 (SSL) 方法的比较,包括SimCLR,评估跨种族的分布转移.
主要成果:
- 在所有培训配置中,自主监督学习 (SSL) 方法显著超过监督学习 (SL) 方法 (p < 0.001).
- 在包括非西班牙裔白人和非裔美国人的平衡数据集培训中,与特定人群培训相比,模型性能得到了改善.
- 平衡的数据集导致了少数民族和健康状况之间的分布转移,有效地减少了模型偏差.
结论:
- 利用SSL方法和对大型,平衡和多样化的数据集的培训可以提高COPD检测模型的性能.
- 这种方法有效地减少了不同族群的性能和检测偏差.
- 公平的AI驱动的COPD诊断医疗保健解决方案需要使用各种培训数据和先进的学习策略.
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