跨机构血细胞形态分析用联邦学习，保护隐私还提升泛化能力
MORPHFED: Federated Learning for Cross-institutional Blood Morphology Analysis
- 各医院本地训练模型，不共享数据只交换参数
- 在多个机构上测试表现优于集中式训练，对新机构适应性强
- 适合医疗数据隐私要求高、资源有限的场景
自动化血细胞形态分析可助力低收入和中等收入国家的血液诊断，但易受染色差异、成像差异和罕见形态的影响。由于隐私法规和数据共享限制，构建集中式数据集捕捉多样性常不可行。我们提出一种用于白细胞形态分析的联邦学习框架，可在不交换训练数据的前提下实现多机构协作训练。基于多个临床机构的血涂片数据，联邦模型学习到鲁棒的、与领域无关的表征，同时完全保障数据隐私。在卷积神经网络与Transformer架构上的评估显示，联邦训练在跨机构性能和对未见机构的泛化能力上均优于集中式训练。这些结果表明，联邦学习是资源受限医疗环境中开发公平、可扩展且泛化的医学影像AI的可行且隐私友好的方法。
原文摘要 · Abstract (English)
Automated blood morphology analysis can support hematological diagnostics in low- and middle-income countries (LMICs) but remains sensitive to dataset shifts from staining variability, imaging differences, and rare morphologies. Building centralized datasets to capture this diversity is often infeasible due to privacy regulations and data-sharing restrictions. We introduce a federated learning framework for white blood cell morphology analysis that enables collaborative training across institutions without exchanging training data. Using blood films from multiple clinical sites, our federated models learn robust, domain-invariant representations while preserving complete data privacy. Evaluations across convolutional and transformer-based architectures show that federated training achieves strong cross-site performance and improved generalization to unseen institutions compared to centralized training. These findings highlight federated learning as a practical and privacy-preserving approach for developing equitable, scalable, and generalizable medical imaging AI in resource-limited healthcare environments.
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