• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>JMIR Medical Informatics >Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment
【24h】

Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment

机译：联合学习甲状腺超声图像分析保护个人信息：真正的医疗保健环境中的验证研究

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background Federated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant. Objective The goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning. Methods A total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution. Results For internal validation, the area under the receiver operating characteristic (AUROC) curve for federated learning was between 78.88% and 87.56%, and the AUROC for conventional deep learning was between 82.61% and 91.57%. For external validation, the AUROC for federated learning was between 75.20% and 86.72%, and the AUROC curve for conventional deep learning was between 73.04% and 91.04%. Conclusions We demonstrated that the performance of federated learning using decentralized data was comparable to that of conventional deep learning using pooled data. Federated learning might be potentially useful for analyzing medical images while protecting patients’ personal information.
机译：背景联合学习是一种分散的机器学习方法;它是一种培训策略，克服了医疗数据隐私法规并概括了深度学习算法。联合学习通过仅共享用于培训的模型和参数来减轻许多系统隐私风险，而无需导出现有的医疗数据集。在这项研究中，我们使用联合学习进行超声图像分析预测甲状腺结节是否是良性的或恶性的。目的是本研究的目标是评估联邦学习的表现是否与传统深度学习相比。方法从6个机构收集了总共8457（5375个恶性，3082个良性）超声图像，并用于联合学习和传统的深度学习。使用了五个深度学习网络（VGG19，Reset50，Resnext50，SE-Resnet50和SE-Resnext50）。使用分层随机抽样，我们选择了20％（1075恶声，616良性）的内部验证的总图像。对于外部验证，我们使用了来自另一个机构的100个超声图像（50个恶性，50个良性）。内部验证的结果，接收器操作特征（Auroc）联合学习曲线的面积介于78.88％和87.56％之间，常规深度学习的助归率为82.61％和91.57％。对于外部验证，联合学习的氧化氢氧化氮介于75.20％和86.72％之间，传统深度学习的菌波曲线在73.04％和91.04％之间。结论我们表明，使用分散数据的联合学习的表现与汇总数据的传统深度学习的表现相当。联合学习可能对保护患者个人信息的同时分析医学图像可能有用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号