论文标题
CXR-FL：使用联合学习的基于深度学习的胸部X射线图像分析
CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learning
论文作者
论文摘要
联合学习使从多中心数据构建共享模型，同时在本地存储培训数据以进行隐私。在本文中，我们提出了使用联合学习方法的胸部X射线图像分析的深度学习模型的评估（称为CXR-FL）。我们研究了联合学习参数对中央模型性能的影响。此外，我们表明，与完整图像相比，如果在感兴趣的区域进行培训，则分类模型的性能较差。但是，将分类模型集中在肺部区域上可能会导致推理期间的病理解释性改善。我们还发现联合学习有助于维持模型的通用性。预先训练的权重和代码可在（https://github.com/sanoscience/cxr-fl）上公开获得。
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).
