  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.
