发表机构
COMSATS University Islamabad; Arab Open University; Kyungdong University Global（COMSATS伊斯兰堡大学; 阿拉伯开放大学; 京东大学全球校区）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对集中式数据存储带来的隐私风险，提出结合优化YOLOv8的联邦学习框架，在分布式CT数据集上实现mAP@50为0.733的肾结石检测，兼顾隐私保护与实时推理。
AI 中文摘要
深度学习的最新创新显著提升了医学图像的诊断能力，尽管这些创新基于集中式数据存储的使用，这对患者隐私和医疗数据安全构成了严重威胁。为解决这一问题，本研究提出了一种联邦学习（FL）模型，该模型与优化的YOLOv8网络相结合，用于在计算机断层扫描（CT）图像上检测肾结石，同时保护患者隐私。所提出的系统可以帮助各种医疗机构在不交换患者信息的情况下共同训练一个通用模型。这确保了遵守如GDPR和HIPAA等数据保护法规。YOLOv8中还包含了残差特征融合和DropBlock正则化等架构改进，以增强检测鲁棒性并减少过拟合。在分布式CT数据集上进行的实验分析表明，联邦YOLOv8模型在IoU阈值为50时的平均精度（mAP）为0.733，并且能够保持数据机密性。此外，其精简设计便于在临床环境中快速边缘部署和实时推理。总之，这些发现表明，当与先进的目标检测模型结合使用时，联邦学习是当代医疗保健中AI辅助诊断的一种安全且高效的解决方案。
英文摘要
Recent innovations in deep learning have significantly enhanced the diagnosis of medical images, although they are based on the use of centralized data storage that pose severe threats to patient privacy and medical data security. To address this issue, this research proposes a Federated Learning (FL) model that is coupled with an optimized YOLOv8 network to detect the kidney stones on a computed tomography (CT) image and at the same time, protect privacy of the patients. The suggested system can help various medical organizations to jointly train a common model without exchanging the information about the patients. This is to ensure that data protection laws like GDPR and HIPAA are adhered to. The residual feature fusion and DropBlock regularization among other architectural improvements are also included in YOLOv8 to enhance detection robustness and minimize overfitting. Experimental analysis carried out on a distributed CT dataset demonstrated that the federated YOLOv8 model has a mAP at 50 of 0.733 and is able to keep the data confidential. Moreover, its lean design facilitates fast edge deployment and real-time inference across a clinical setting. Altogether, these findings indicate that Federated Learning is a safe and efficient solution to AI-assisted diagnosis in contemporary healthcare when combined with the use of sophisticated object detection models.
