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.

翻译：颅内出血是一种潜在致命疾病，其临床表现具有高度多样性，且在全球各临床中心之间存在显著差异。基于深度学习的解决方案已开始建模脑结构间的复杂关系，但仍难以实现泛化。虽然收集更多样化的数据是最自然的方法，但隐私法规常限制医疗数据的共享。我们提出了联邦场景图生成的首次应用。研究表明，我们的模型能够充分利用训练数据多样性的提升。在场景图生成任务中，与在单一中心化数据集上训练的模型相比，我们的模型在不同数据集间可多召回高达20%的临床相关关系。在联邦环境中学习结构化数据表征，可为开发新方法开辟道路，这些方法能够利用这种更精细的信息，在客户端之间实现更有效的正则化。