用联邦学习实现跨医院面部图像精准诊断罕见病，保护隐私且性能超90%。
Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning
- 跨机构联邦学习，不共享原始图像，仅交换特征和模型参数。
- 诊断准确率保留集中式训练的90%以上，适应不同数量医院和数据差异。
- 适合医疗数据隐私要求高、需协作研发的医院与研究团队。
机器学习在面部畸形分析中展现潜力，特征性面部表型可为罕见遗传病提供诊断线索。GestaltMatcher作为该领域的领先框架，在多项研究中证明了临床价值，但其依赖集中式数据集限制了进一步发展，因患者数据被各机构隔离并受严格隐私法规约束。我们提出基于跨域水平联邦学习框架的联邦GestaltMatcher服务，使医院可在不共享患者图像的情况下协同训练全局集成特征提取器。患者数据被映射至共享潜在空间，通过隐私保护的核矩阵计算框架实现综合征推断与发现，同时保障数据机密性。新参与者可直接采用前序训练轮次的全局特征提取器与核配置，从中获益并贡献数据。实验表明，该联邦服务保留超过90%的集中式性能，并对不同参与方数量及异构数据分布保持鲁棒性。
原文摘要 · Abstract (English)
Machine learning has shown promise in facial dysmorphology, where characteristic facial features provide diagnostic clues for rare genetic disorders. GestaltMatcher, a leading framework in this field, has demonstrated clinical utility across multiple studies, but its reliance on centralized datasets limits further development, as patient data are siloed across institutions and subject to strict privacy regulations. We introduce a federated GestaltMatcher service based on a cross-silo horizontal federated learning framework, which allows hospitals to collaboratively train a global ensemble feature extractor without sharing patient images. Patient data are mapped into a shared latent space, and a privacy-preserving kernel matrix computation framework enables syndrome inference and discovery while safeguarding confidentiality. New participants can directly benefit from and contribute to the system by adopting the global feature extractor and kernel configuration from previous training rounds. Experiments show that the federated service retains over 90% of centralized performance and remains robust to both varying silo numbers and heterogeneous data distributions.
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