公平的扩散:通过公平的贝叶斯扰动来提高隐性扩散模型的公平性
Yan Luo1,2,3, Muhammad Osama Khan4, Congcong Wen4,5
1Harvard AI and Robotics Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114 USA.
Science advances
|April 4, 2025
概括
医疗保健中的生成人工智能显示在人口统计学中对图像生成的偏见. 一个新的模型,FairDiffusion和数据集,FairGenMed,旨在提高公平性和质量,以获得公平的AI利益.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 计算机视觉 计算机视觉
背景情况:
- 生成型人工智能,特别是扩散模型,在文本到图像合成方面表现出色.
- 这些模型对合成数据生成和医疗保健中的医疗培训充满希望.
- 人口分组之间的图像生成质量的一致性存在担忧.
研究的目的:
- 进行医学文本到图像扩散模型中公平性的全面分析.
- 提出和评估一个公平意识的模型,以减轻发现的偏见.
- 引入一个新的数据集来研究医疗生成模型中的公平性.
主要方法:
- 对图像生成中的人口差异进行了稳定扩散模型的评估.
- 开发了FairDiffusion,一个以公平意识为基础的隐性扩散模型.
- 策划了FairGenMed,一个专门用于医学生成AI公平性研究的数据集.
- 在皮肤镜像 (HAM10000) 和胸部X射线 (CheXpert) 上评估了FairDiffusion.
主要成果:
- 确定了稳定扩散的图像生成在性别,种族和种族之间存在的显著差异.
- FairDiffusion 显示了更好的图像质量和临床特征的语义对齐.
- 该模型在各种医学成像模式中被证明有效.
结论:
- 公平性是医疗保健中生成人工智能的关键考虑因素.
- 公平传播 (FairDiffusion) 和公平基因医学 (FairGenMed) 代表了公平基因学习的进步.
- 这些贡献促进了生成AI在医疗领域的公平应用.
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