Domain Generalized Fundus Image Segmentation via Dual-Level Mixing

Single domain generalization plays a vital role in signal processing tasks, which is capable of extracting domain-invariant knowledge from a single source domain such that the learned model can be generalizable to unseen domains. However, many existing methods require the help of auxiliary tasks, bringing extra computation cost. Aiming at this pitfall, this study proposes Dual-Level Mixing (DLM) to boost the diversity of the single source domain and enhance the generalization performance. Specifically, at the input level, we apply different image augmentations to get different variants of the same image. Patches from augmented images are spatially mixed to get a perturbed image as the input, which enlarges the scale of training data and boosts the diversity. Meanwhile, at the feature level, we characterize feature statistics as Gaussian distributions. Then, we resample the feature statistics to re-normalize the latent features, which simulates the potential style variance of different domains. In summary, the proposed DLM method synergies input-level and feature-level mixing strategies, leading to enhanced generalization performance. Experimental results of cross-domain fundus image segmentation demonstrate that either input-level mixing or feature-level mixing can effectively promote the performance of domain generalization. Moreover, the collaboration of dual-level mixing strategies leads to superior or comparable performance to domain adaptation counterparts that rely on target data.