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联合伪标签:一个以数据为中心,保护隐私的医疗图像细分框架

Sidratul Montaha, Rashik Rahman, Tapotosh Ghosh

IEEE journal of biomedical and health informatics

|December 8, 2025

概括

一个新的框架DCFed通过使用未标记的公共数据进行隐私保护培训来增强医疗图像细分. 它的性能优于传统的联合学习,提高了概括性和准确性,而无需共享敏感的患者信息或模型参数.

科学领域:

医学图像分析医学图像分析
人工智能的人工智能
数据隐私数据隐私数据

背景情况:

患者隐私问题和不一致的注释限制了医疗数据共享的深度学习.
目前的联合学习 (FL) 方法面临着统一架构,隐私风险和通信成本的挑战.

研究的目的:

引入DCFed,一个以数据为中心,半监督的框架,用于保护隐私的医疗图像细分.
解决医疗AI中数据共享和传统FL的局限性.

主要方法:

在公开的,没有注释的数据集上使用伪标签和不确定性估计.
实现了修改后的U-Net架构,在客户端级别使用剩余块,ASPP和CBAM.
开发了一个以数据为中心,半监督的方法,避免原始数据和参数共享.

主要成果:

与本地培训相比,DCFed在乳腺癌超声波和皮肤癌皮肤透视数据集上的性能提高了8.9%,3.7%.
在多客户端场景中,FedAvg和FedNova在医疗成像任务中的表现都超过了FedAvg和FedNova.
与集中培训相比,在当地数据和参数共享FL方面取得了卓越的成果.

结论:

DCFed为医疗图像细分提供了一个可扩展和保护隐私的解决方案.
该框架有效地利用公共数据来提高模型的通用性和性能.
在隐私和效率方面超越现有的FL和集中式培训方法.

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