Federated learning enables building a shared model from multicentre data while storing the training data locally for privacy. In this paper, we present an evaluation (called CXR-FL) of deep learning-based models for chest X-ray image analysis using the federated learning method. We examine the impact of federated learning parameters on the performance of central models. Additionally, we show that classification models perform worse if trained on a region of interest reduced to segmentation of the lung compared to the full image. However, focusing training of the classification model on the lung area may result in improved pathology interpretability during inference. We also find that federated learning helps maintain model generalizability. The pre-trained weights and code are publicly available at (https://github.com/SanoScience/CXR-FL).
翻译:联邦学习有助于从多中心数据中建立一个共享模型,同时在当地为隐私而储存培训数据; 在本文件中,我们用联邦学习方法对胸前X射线图像分析的深层学习模型(称为CXR-FL)进行了评价(称为CXR-FL),我们研究了联邦学习参数对中央模型性能的影响;此外,我们表明,如果在感兴趣的区域培训中将肺部的分解减为完整图像,分类模型的收效会更差;然而,在推断期间,将分类模型的培训重点放在肺部地区可能会改善病理学解释能力;我们还发现,联邦学习有助于保持模型的通用性;预先培训的重量和代码可在(https://github.com/Sanoscience/CXR-FL)上公开查阅(https://gthub.com/Science/CXR-FL)。