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有效的一次性联合学习医疗数据使用知识蒸与图像合成和客户端模型适应

Myeongkyun Kang¹, Philip Chikontwe², Soopil Kim¹

¹Department of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA.

Medical image analysis

|July 17, 2025

概括

这项研究引入了一种新的一次性联合学习 (FL) 方法,使用混合生成的合成图像与噪音. 这种方法可以减少医疗图像分类任务中的过和计算成本.

科学领域:

人工智能的人工智能
机器学习机器学习
医疗成像医学成像

背景情况:

一次性联合学习 (FL) 在多次沟通回合不切实际时至关重要.
现有的知识蒸 (KD) 方法在一次性FL中与过度装配和高计算需求作斗争.
医学图像分析需要高效准确的分布式学习模型.

研究的目的:

为医疗图像分类开发一个高效和强大的一次性联合学习框架.
在联合学习场景中解决过度拟合和减少计算复杂性.
通过合成数据,加强从客户向全球模型的知识转移.

主要方法:

提出了一种新型的一次性FL框架,利用混合生成具有多种结构噪声的伪中间样本.
通过更新批量规范化统计数据来实现适应噪声的客户端模型,以减轻域差异.
在全球模型更新中使用原始和适应噪声的客户端模型之间的代知识蒸 (KD).

主要成果:

拟议的方法显著提高了培训样本的多样性,有效地防止过度装配.
重复使用合成图像可以减少计算资源,提高整体训练效率.
对多个医学图像分类数据集的广泛评估证实了该方法在现有方法上的优越性.

结论:

新型的一次性FL框架有效地解决了医疗图像分类中的过拟合和计算挑战.
使用混合生成的噪音合成图像提高了模型的稳定性和训练效率.
适应噪声的客户端模型和KD策略改善了知识转移和整体绩效.

关键词:

客户端模型的适应.图像合成图像合成知识的蒸知识的蒸.噪声噪声噪声一次性联合学习学习.

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