深度解剖联邦网络（Dafne）：一个用于持续协作改进基于深度学习的医学图像分割的开源客户端-服务器框架
Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation
AI总结：
Dafne是一个开源客户端-服务器联邦学习框架，通过增量学习持续改进医学图像分割，实验显示分割精度随时间显著提升并具有良好的泛化能力。
摘要：
目的：介绍并评估Dafne（深度解剖联邦网络），这是一个免费可用的去中心化协作深度学习系统，通过联邦增量学习对放射图像进行语义分割。材料与方法：Dafne是采用客户端-服务器架构的自由软件。客户端是一个高级用户界面，应用存储在服务器上的深度学习模型于用户数据，并允许用户检查和细化预测结果。随后在客户端执行增量学习，并将结果发送回服务器，集成到根模型中。Dafne通过在38个小腿MRI数据集上评估跨模型代的性能提升，以及分析真实世界使用统计数据（n=639个使用案例）进行本地评估。结果：Dafne展示了语义分割准确性随时间的统计显著改善（在本地验证集上，Dice相似系数平均每代增加0.007点，p<0.001）。定性上，模型在各种放射图像类型上表现出增强的性能，包括那些不在初始训练集中的类型，表明模型具有良好的泛化能力。结论：Dafne展示了分割质量随时间的改善，显示出学习和泛化的潜力。
英文摘要：
Purpose: To present and evaluate Dafne (deep anatomical federated network), a freely available decentralized, collaborative deep learning system for the semantic segmentation of radiological images through federated incremental learning. Materials and Methods: Dafne is free software with a client-server architecture. The client side is an advanced user interface that applies the deep learning models stored on the server to the user's data and allows the user to check and refine the prediction. Incremental learning is then performed at the client's side and sent back to the server, where it is integrated into the root model. Dafne was evaluated locally, by assessing the performance gain across model generations on 38 MRI datasets of the lower legs, and through the analysis of real-world usage statistics (n = 639 use-cases). Results: Dafne demonstrated a statistically improvement in the accuracy of semantic segmentation over time (average increase of the Dice Similarity Coefficient by 0.007 points/generation on the local validation set, p < 0.001). Qualitatively, the models showed enhanced performance on various radiologic image types, including those not present in the initial training sets, indicating good model generalizability. Conclusion: Dafne showed improvement in segmentation quality over time, demonstrating potential for learning and generalization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
