WWFedCBMIR:全球联合基于内容的医疗图像检索
Zahra Tabatabaei1,2, Yuandou Wang3, Adrián Colomer2,4
1Department of Artificial Intelligence, Tyris Tech S.L., 46021 Valencia, Spain.
Bioengineering (Basel, Switzerland)
|October 28, 2023
概括
联合基于内容的医学图像检索 (FedCBMIR) 使用联合学习 (FL) 来培训模型而无需共享数据,提高性能并减少病理学家的培训时间.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 病理学 病理学 病理学
背景情况:
- 基于内容的医学图像检索 (CBMIR) 帮助病理学家找到类似的病例.
- 训练CBMIR模型需要各种各样的全幻灯片图像 (WSIs),由于数据共享法规,这些图像很难获得.
- 联合学习 (FL) 提供了一个解决方案,可以在不影响数据隐私的情况下进行去中心化的模型培训.
研究的目的:
- 提出一个基于联邦内容的医疗图像检索 (FedCBMIR) 工具.
- 为应对获取各种医疗数据集以培训CBMIR模型的挑战.
- 通过联合学习来提高CBMIR的性能和效率.
主要方法:
- 为CBMIR开发了一个无监督特征提取器 (FE).
- 实施了联合学习框架 (FedCBMIR) 以将FE分发给合作培训中心.
- 评估了FedCBMIR使用两个实验与乳腺癌数据集 (BreaKHis,Camelyon17) 在不同的客户端和放大.
主要成果:
- 美联储CBMIR显著提高了F1分数:从96%提高到98.1% (Camelyon17) 和95%提高到98.4% (BreaKHis).
- 在最初的实验中,训练时间减少了11.44小时.
- 在BreaKHis实验中使用通用模型获得了高F1分数 (94-98%),完成训练速度比BreaKHis实验快25.53小时.
结论:
- 在没有集中敏感医疗数据的情况下,FedCBMIR有效地使用联邦学习来训练CBMIR模型.
- 拟议的方法提高了检索准确性,并大大减少了模型训练时间.
- 在医疗机构中开发强大的CBMIR工具,FedCBMIR展示了一种有前途的方法.
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