基于PPPML-HMI的个性化与隐私保护联邦异构医学图像分析
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI
AI总结：
本文提出开源框架PPPML-HMI，通过集成PerFedAvg和同态加密循环安全聚合，首次在联邦异构医学图像分析中同时实现个性化与隐私保护，并在分类和分割任务上验证了其性能提升、隐私保护能力和鲁棒性。
摘要：
在医学影像领域，由于医院使用不同的模型和设备设置，异构数据普遍存在。然而，目前很少有开源框架能够在不要求修改现有模型结构或共享任何私有数据的情况下，同时实现个性化与隐私保护的联邦异构医学图像分析。本文提出了PPPML-HMI，一种用于个性化与隐私保护联邦异构医学图像分析的开源学习范式。据我们所知，通过集成PerFedAvg算法并设计我们新颖的基于同态加密算法的循环安全聚合，首次在联邦场景下同时实现了个性化和隐私保护。为了展示PPPML-HMI的实用性，我们将其应用于一个模拟分类任务，即从RAD-ChestCT数据集中对健康人和患者进行分类，以及一个真实世界分割任务，即从COVID-19 CT扫描中分割肺部感染。对于真实世界任务，在异构场景下，PPPML-HMI与传统联邦学习相比，平均Dice分数提高了约5%。同时，我们应用改进的深度梯度泄漏来模拟对抗性攻击，并展示了PPPML-HMI强大的隐私保护能力。通过将PPPML-HMI应用于具有不同神经网络、不同用户数量和样本量的两个任务，我们进一步证明了PPPML-HMI的强大鲁棒性。
英文摘要：
Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data. In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. For the real-world task, PPPML-HMI achieved $\sim$5\% higher Dice score on average compared to conventional FL under the heterogeneous scenario. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI.
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。
