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The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...
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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

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    此摘要是机器生成的。

    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.

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    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.