通过对抗性受保护属性的公平超声波诊断,意识到潜伏嵌入的乱
Zikang Xu1,2, Fenghe Tang1,2, Quan Quan3
1School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, Anhui, China.
NPJ digital medicine
|May 17, 2025
概括
这项研究介绍了APPLE,这是一种减少人工智能超声波诊断偏差的新方法,而不改变现有模型. 果增强了医疗图像细分中的公平性,以获得更公平的医疗保健.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 深度学习可以提高超声波诊断和病变细分的准确性.
- 现有的模型显示性别和年龄之间的绩效差异,导致偏见的医学诊断.
- 人工智能诊断工具的公平性对于公平的医疗保健结果至关重要.
研究的目的:
- 提出一种新的方法,APPLE,以减轻基于深度学习的超声波图像细分中的不公平.
- 解决人工智能诊断模型在性别和年龄等敏感属性的性能差异.
- 提高诊断公平性,而不会改变先前存在的基准模型的参数.
主要方法:
- 果使用生成对抗网络学习潜空间中的公平扰动.
- 该方法被设计为模型不可知,适用于各种骨干架构.
- 对公共和内部超声波图像数据集进行了实验.
主要成果:
- 在所有经过测试的敏感属性中,APPLE显著改善了细分和诊断公平性.
- 与基线模型相比,拟议的方法显示了更高的公平性.
- 在不同的骨干架构中验证了有效性.
结论:
- 公平性是医学图像细分AI的关键考虑因素.
- 果提供了一个可行的策略,以提高AI辅助医学诊断的公平性.
- 这项工作有助于通过人工智能开发一个更加公正和公正的医疗保健系统.
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