用于MRI重建的跨模态纵向联邦学习
Cross-Modal Vertical Federated Learning for MRI Reconstruction
AI总结：
针对多医院MRI模态不一致的纵向联邦学习问题，提出Fed-CRFD框架，通过特征解耦与重叠样本一致性约束缓解域偏移并提升重建性能。
摘要：
联邦学习使多家医院能够在不泄露隐私的情况下协同学习一个共享模型。现有方法通常采用一个共同假设，即来自不同医院的数据具有相同的模态。然而，这样的设置在实际应用中很难完全满足，因为不同医院之间的成像指南可能存在差异，这使得具有同一组模态的个体数量有限。为此，我们形式化了这一实际且具有挑战性的跨模态纵向联邦学习任务，其中来自多家医院的形状数据具有不同模态，同时包含少量从相同个体采集的多模态数据。为应对这种情况，我们开发了一个新颖框架，即Federated Consistent Regularization constrained Feature Disentanglement（Fed-CRFD，联邦一致性正则化约束特征解耦），通过有效挖掘重叠样本（具有多模态的个体）并解决由不同模态导致的域偏移问题来提升MRI重建。特别地，我们的Fed-CRFD包含一种客户端内特征解耦方案，将数据解耦为模态不变特征和模态特定特征，其中模态不变特征被用于缓解域偏移问题。此外，还专门针对重叠样本提出了跨客户端潜在表示一致性约束，以进一步对齐从不同模态提取的模态不变特征。因此，我们的方法能够在缓解域偏移问题的同时，充分利用来自医院的多源数据。在两个典型MRI数据集上的大量实验表明，我们的网络明显优于最先进的MRI重建方法。源代码将在本工作发表后公开发布。
英文摘要：
Federated learning enables multiple hospitals to cooperatively learn a shared model without privacy disclosure. Existing methods often take a common assumption that the data from different hospitals have the same modalities. However, such a setting is difficult to fully satisfy in practical applications, since the imaging guidelines may be different between hospitals, which makes the number of individuals with the same set of modalities limited. To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which shape data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals. To tackle such a situation, we develop a novel framework, namely Federated Consistent Regularization constrained Feature Disentanglement (Fed-CRFD), for boosting MRI reconstruction by effectively exploring the overlapping samples (individuals with multi-modalities) and solving the domain shift problem caused by different modalities. Particularly, our Fed-CRFD involves an intra-client feature disentangle scheme to decouple data into modality-invariant and modality-specific features, where the modality-invariant features are leveraged to mitigate the domain shift problem. In addition, a cross-client latent representation consistency constraint is proposed specifically for the overlapping samples to further align the modality-invariant features extracted from different modalities. Hence, our method can fully exploit the multi-source data from hospitals while alleviating the domain shift problem. Extensive experiments on two typical MRI datasets demonstrate that our network clearly outperforms state-of-the-art MRI reconstruction methods. The source code will be publicly released upon the publication of this work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
