TransFair:将公平性从眼病分类转移到进展预测.
Min Shi1, Leila Gheisi1, Chee-Hung Henry Chu1
1School of Computing and Informatics, University of Louisiana at Lafayette, LA, USA.
Artificial intelligence in medicine
|December 11, 2025
概括
TransFair提高了人工智能 (AI) 的公平性,用于预测眼部疾病的进展. 这种方法通过将公平性从分类转移到进展预测模型来增强医疗保健AI中的人口公平性.
科学领域:
- 眼科医生 眼科 眼科
- 医疗人工智能 医疗人工智能
- 计算机科学 计算机科学
背景情况:
- 疾病分类中的人工智能 (AI) 节省了成本,改善了获得护理的机会.
- 人工智能公平性存在担忧,特别是它对弱势群体的不成比例影响.
- 现有的AI公平性分类方法可能无法扩展到疾病进展预测,原因是有限的多样化的纵向数据.
研究的目的:
- 引入TransFair,这是一种新的方法,用于提高眼部疾病进展预测中的人口公平性.
- 确保在将知识从公平的疾病分类模型转移到进展预测模型时保持公平.
主要方法:
- 训练了一个以公平意识为基础的EfficientNet (FairEN) 模型,使用广泛的眼部疾病分类数据.
- 将FairEN模型调整为使用知识蒸的公平进展预测模型.
- 将分类和进展预测模型之间的潜在特征距离最小化,以保持公平性.
主要成果:
- 通过使用2D和3D视网膜图像评估TransFair用于眼部疾病分类和进展预测.
- 证明TransFair有效地提高了预测眼部疾病进展的群体公平性.
- 在公平性增强的眼部疾病分类中,FairEN显示出有效性.
结论:
- TransFair在预测眼部疾病进展方面成功提高了人口统计学公平性.
- 知识蒸方法有效地将公平性从分类转移到进展预测.
- TransFair为医疗预后中的公平人工智能提供了一个有希望的解决方案.
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