面向临床适用的基于大型模型的隐私保护多体细分:对结肠镜进行联合的LoRA方法
IEEE journal of biomedical and health informatics
|December 2, 2025
概括
PolypSAMFL使用联合学习和低级适应 (LoRA) 的任何细分模型 (SAM) 增强了结肠镜聚细分. 这种保护隐私的方法实现了高准确性,改善了临床AI工作流程中的聚体检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 准确的结肠镜聚细分对于检测病变和优化临床工作流程至关重要.
- 在医疗保健中部署大型人工智能模型是具有挑战性的,因为隐私问题,计算需求和数据变化.
研究的目的:
- 引入PolypSAMFL,这是一个用于保护隐私的新型框架,高精度的息肉细分.
- 在联合学习 (FL) 方法中,将低级适应 (LoRA) 与分段任何模型 (SAM) 整合在一起.
主要方法:
- 使用分段任何模型 (SAM) 和低级适应 (LoRA) 的联合学习 (FL) 实现在分布式数据集上进行隐私保护模型培训.
- 结了大多数SAM参数,仅微调紧型LoRA模块以减少通信开销.
- 实现了边界感知损失函数和多分辨率面具合成,以改进聚合体边界划分.
主要成果:
- 在四个公共结肠镜数据集上获得了0.987的Dice平均得分和0.976的交叉-超过-联盟 (IoU).
- 与最先进的方法相比,在保持数据局部性的同时,表现出卓越的性能.
- 显著降低了与联合学习相关的通信成本.
结论:
- 波利普SAMFL为人工智能驱动的结肠镜工作流提供了一个可扩展,保护隐私的解决方案.
- 该框架提高了多片细分的准确性和可靠性,与医疗保健隐私法规和资源限制保持一致.
- 在胃肠内镜中对真实世界AI应用的验证的临床实用性.
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