Modern deep neural networks struggle to transfer knowledge and generalize across diverse domains when deployed to real-world applications. Currently, domain generalization (DG) is introduced to learn a universal representation from multiple domains to improve the network generalization ability on unseen domains. However, previous DG methods only focus on the data-level consistency scheme without considering the synergistic regularization among different consistency schemes. In this paper, we present a novel Hierarchical Consistency framework for Domain Generalization (HCDG) by integrating Extrinsic Consistency and Intrinsic Consistency synergistically. Particularly, for the Extrinsic Consistency, we leverage the knowledge across multiple source domains to enforce data-level consistency. To better enhance such consistency, we design a novel Amplitude Gaussian-mixing strategy into Fourier-based data augmentation called DomainUp. For the Intrinsic Consistency, we perform task-level consistency for the same instance under the dual-task scenario. We evaluate the proposed HCDG framework on two medical image segmentation tasks, i.e., optic cup/disc segmentation on fundus images and prostate MRI segmentation. Extensive experimental results manifest the effectiveness and versatility of our HCDG framework.

翻译：现代深度神经网络在实际应用部署时，难以在不同域之间迁移知识和泛化。当前，域泛化（DG）技术被引入以从多个域学习通用表示，从而提升网络对未见域的泛化能力。然而，以往的DG方法仅关注数据级一致性方案，未考虑不同一致性方案间的协同正则化。本文提出一种新颖的层次一致性域泛化框架（HCDG），通过协同整合外在一致性与内在一致性。具体而言，在外在一致性层面，我们利用多个源域知识强制实现数据级一致性；为增强该一致性，我们在基于傅里叶的数据增强中设计新型振幅高斯混合策略（称为DomainUp）。在内在一致性层面，我们在双任务场景下对同一实例实施任务级一致性。我们在两项医学图像分割任务（眼底图像视杯/视盘分割与前列腺MRI分割）上评估所提HCDG框架。大量实验结果表明了HCDG框架的有效性与通用性。