病理学基础模型的以知识为导向的适应有效地提高了跨领域的概括性和人口公平性
Yanyan Huang1, Weiqin Zhao1, Zhengyu Zhang2
1School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China.
Nature communications
|December 12, 2025
概括
计算病理学的基础模型可能会有偏见. 一个新的框架FLEX通过将强大的特征从偏见中解开来提高AI的概括性和公平性,为各种设置增强病理AI.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能
- 生物医学信息学是生物医学信息学.
背景情况:
- 计算病理学的基础模型表现出特定站点和人口统计学偏见.
- 这些偏见限制了AI模型在病理学中的通用性和公平性.
- 现有的模型在不同的数据集和患者群体中难以可靠地执行.
研究的目的:
- 引入FLEX,一个新的框架,旨在减轻计算病理学基础模型中的偏差.
- 为了提高AI模型对病理学的概括性和公平性.
- 为各种临床应用开发一个更公平的AI解决方案.
主要方法:
- 开发了FLEX,这是一个利用特定任务信息瓶的框架,以视觉和文本领域的知识为指导.
- 在16个临床任务 (>9,900个幻灯片) 中使用了三大队列 (TCGA,CPAC,内部).
- 评估了零射击概括,对未见的队伍的表现,以及跨人口群体的公平性.
主要成果:
- FLEX向外部队列展示了优越的零射击概括,超过了基线模型.
- 该框架显著缩小了可见和不可见领域之间的绩效差距.
- 综合的公平性分析证实了FLEX在缓解人口差异方面的有效性.
- FLEX展示了多功能性和可扩展性,与各种基础模型和MIL架构兼容.
结论:
- 在计算病理学中,FLEX是开发更具普遍性和公平性的AI的有希望的解决方案.
- 该框架有效地将强大的病理特征从特定地点和人口统计学偏见中解脱出来.
- 在各种临床病理学环境中,FLEX有助于推进公平可靠的AI应用.
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