联邦学习+场景图生成，提升脑出血诊断的跨中心泛化能力
Federated Voxel Scene Graph for Intracranial Hemorrhage
- 采用联邦场景图生成框架，联合多中心数据训练
- 跨数据集临床相关关系召回率提升20%
- 适合需要保护隐私又需跨机构协作的医学影像研究
颅内出血是一种潜在致命性疾病，其表现形式多样且在不同医疗机构间差异显著。基于深度学习的解决方案开始建模脑部结构间的复杂关系，但仍难以实现良好泛化。尽管增加多样化数据是最直接的方法，但隐私法规常限制医疗数据共享。我们首次提出联邦场景图生成的应用。结果表明，模型可利用更多样化的训练数据。在场景图生成任务中，相比仅在单一中心数据上训练的模型，本方法在跨数据集上可多召回20%的临床相关关系。在联邦设置下学习结构化数据表示，有望推动新方法发展，更有效地利用细粒度信息进行客户端间正则化。
原文摘要 · Abstract (English)
Intracranial Hemorrhage is a potentially lethal condition whose manifestation is vastly diverse and shifts across clinical centers worldwide. Deep-learning-based solutions are starting to model complex relations between brain structures, but still struggle to generalize. While gathering more diverse data is the most natural approach, privacy regulations often limit the sharing of medical data. We propose the first application of Federated Scene Graph Generation. We show that our models can leverage the increased training data diversity. For Scene Graph Generation, they can recall up to 20% more clinically relevant relations across datasets compared to models trained on a single centralized dataset. Learning structured data representation in a federated setting can open the way to the development of new methods that can leverage this finer information to regularize across clients more effectively.
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