咖啡馆:使用联邦自主监督学习进行胸部X射线分析,用于儿科COVID-19检测
Abhijeet Parida1, Syed Muhammad Anwar1,2, Malhar P Patel3
1Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, 111 Michigan Ave, Washington, DC 20010, USA.
概括
一个新的AI模型使用联合自主监督学习 (FSSL) 来改善儿童胸部X射线 (CXR) 中的COVID-19检测. 这种方法提高了视觉转换器 (ViT) 的性能,实现了在儿童中诊断COVID-19的更高准确性.
科学领域:
- 医疗成像中的人工智能
- 放射学和诊断成像 放射学和诊断成像
- 计算病理学计算病理学
背景情况:
- 胸部X射线 (CXR) 对于评估儿科肺部疾病至关重要,包括与COVID-19相关的疾病.
- 准确和快速的COVID-19儿童诊断对于及时的临床管理至关重要.
- 现有的人工智能模型可能需要大型的,集中的数据集,这给隐私和可扩展性带来了挑战.
研究的目的:
- 在儿科CXR中开发和评估人工智能驱动的二元COVID-19与非COVID-19分类的解决方案.
- 通过联邦自主监督学习 (FSSL) 框架,提高用于COVID-19检测的视觉转换器 (ViTs) 的性能.
- 确保医疗图像分析的隐私意识和可扩展的方法.
主要方法:
- 实施一个使用视觉转换器 (ViT) 架构的联合自主监督学习 (FSSL) 框架.
- 在成人CXR数据上进行ViT的自我监督预训,然后对儿科CXR数据集进行微调.
- 在犀牛健康联合计算平台 (FCP) 上部署,以保护隐私的分布式学习.
- 使用联邦SSL (CAFES) 模型开发胸部X射线分析.
主要成果:
- 与完全监督模型相比,FSSL预训练的ViT模型 (CAFES) 在儿科CXR中表现出更好的COVID-19检测准确性.
- 在CAFES模型中,精度回忆曲线 (AUPR) 下的面积为0.952.2.
- 这比儿科COVID-19诊断的完全监督模型显著改善0.231点.
结论:
- 利用ViT与FSSL预训练提供了一种有效的策略,用于从儿科CXR中诊断COVID-19.
- 基于分布式联合学习的自我监督预培训提高了诊断性能,同时保持了数据隐私.
- 这种对隐私有意识的方法,与HIPAA指导方针保持一致,显示出在医学成像AI中更广泛的应用的前景.
关键词:
诊断COVID-19的诊断结果是什么胸部X射线 胸部X射线联合学习学习 (Federated Learning) 是一种联合学习.联合自主监督学习学习医学成像医学成像儿科患者 儿科患者维护隐私 - 维护隐私视觉转换器 视觉转换器更多相关视频
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