HSGO:协调群体学习与指导优化用于多中心的sMRI阿尔茨海默氏症疾病的分类
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
本研究引入了带有指导优化 (HSGO) 的协调群体学习,以使用联合学习改进阿尔茨海默病 (AD) 的分类. HSGO增强了多中心的协作和隐私,同时在AD诊断中实现了竞争性表现.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 开发强大的阿尔茨海默病 (AD) 分类模型需要广泛的多中心数据,但数据聚合引发了隐私问题.
- 现有的联合学习 (FL) 和群学习 (SL) 方法由于数据异质性和中心间的阶级不平衡而面临性能限制.
研究的目的:
- 提出一个新的协调群体学习框架与指导优化 (HSGO) 保护隐私,增强多中心协作AD分类.
- 为了提高通用和个性化的AD分类模型的稳定性和性能,在各种数据集中进行训练.
主要方法:
- 在HSGO框架内实施了一个类平衡损失函数来训练一个强大的通用模型.
- 利用指导优化来使个性化模型与通用模型保持一致,避免额外的功能提取.
- 开发了一个动态的特征相似性存储机制,以支持个性化的模型培训.
主要成果:
- 与五种基准方法相比,HSGO在两个多中心数据分区场景中表现出了竞争力.
- 层级相关性传播 (LRP) 分析表明HSGO在通过整合本地和全球特征来识别AD的关键大脑区域的潜力.
结论:
- 在AD分类中,HSGO为保护隐私的多中心协作提供了有效的解决方案.
- 该框架有望提高模型性能,并可能有助于识别阿尔茨海默病关键的神经成像生物标志物.
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