用联邦学习实现放疗规划的高效隐私保护预测
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy
- 基于gRPC构建通信架构，支持中心化与去中心化联邦学习
- 在三个放疗预测任务中表现稳定，模型性能接近集中式训练
- 适合医疗数据隐私敏感场景下的多机构协作建模
背景：深度学习有望提升放疗计划的效率与一致性，但因机构间数据稀缺性和异质性导致模型泛化能力差，临床应用受限。尽管跨机构数据聚合可缓解此问题，但患者隐私和技术障碍使数据共享难以实现。目的：本文提出FedKBP+，一个面向放疗规划实际应用的完整联邦学习平台，解决这一困境。方法：我们基于Google远程过程调用（gRPC）构建统一通信栈，支持同一工作站或跨多工作站的参与者通信。除支持现有开源框架中的常见中心化联邦学习策略外，还提供完全去中心化的联邦学习模式，参与者通过点对点通信直接交换模型权重。我们在三个预测任务上使用尺度注意力网络（SA-Net）作为预测模型评估了FedKBP+。结论：结果表明，FedKBP+高效、稳健且有效，展现出作为放疗领域联邦学习平台的巨大潜力。
原文摘要 · Abstract (English)
Background: Deep learning has potential to improve the efficiency and consistency of radiation therapy planning, but clinical adoption is hindered by the limited model generalizability due to data scarcity and heterogeneity among institutions. Although aggregating data from different institutions could alleviate this problem, data sharing is a practical challenge due to concerns about patient data privacy and other technical obstacles. Purpose: This work aims to address this dilemma by developing FedKBP+, a comprehensive federated learning (FL) platform for predictive tasks in real-world applications in radiotherapy treatment planning. Methods: We implemented a unified communication stack based on Google Remote Procedure Call (gRPC) to support communication between participants whether located on the same workstation or distributed across multiple workstations. In addition to supporting the centralized FL strategies commonly available in existing open-source frameworks, FedKBP+ also provides a fully decentralized FL model where participants directly exchange model weights to each other through Peer-to-Peer communication. We evaluated FedKBP+ on three predictive tasks using scale-attention network (SA-Net) as the predictive model. Conclusions: Our results demonstrate that FedKBP+ is highly effective, efficient and robust, showing great potential as a federated learning platform for radiation therapy.
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