通过知识增强初始化来提高对新疾病的学习,用于联邦适配器调调
IEEE transactions on medical imaging
|August 7, 2025
概括
联合知识增强初始化 (FedKEI) 改善了医疗保健中的基础模型的适配器调整. 该框架通过利用过去的知识来加强保护隐私的合作,以更快地适应新的医疗任务.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 联合学习 (FL) 允许在医疗保健中保持隐私的协作.
- 基础模型 (FMs) 越来越多地用于 FL 的适配器调,用于医疗应用.
- 快速发展的医疗保健需要快速适应新的任务/疾病,利用过去的经验.
研究的目的:
- 引入联邦知识增强初始化 (FedKEI) 以提供信息的适配器调整初始化.
- 从过去的知识中利用跨客户和跨任务的转移.
- 能够有效地适应新的医疗任务或疾病.
主要方法:
- FedKEI使用全球集群来实现跨任务的知识概括.
- 优化聚合权重 (集群间和集群内) 以实现个性化知识传输.
- 使用双级优化方案进行重量协作学习.
主要成果:
- 在适应新疾病方面,FedKEI表现出了显著的优势.
- 在皮肤学,胸部X射线和视网膜OCT数据集上进行的实验.
- 在适应新任务时,超越了最先进的方法.
结论:
- FedKEI提供了一种有效的方法,用于启动医疗保健联合学习中的适配器.
- 该框架通过利用先前的知识,提高了应对新医疗挑战的适应能力.
- FedKEI 提高了基础模型在不同医疗领域的效率和性能.
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