Beyond the Distribution: Perturbation Toward Domain Distribution Boundary for Strengthening Generalizable Semi-Supervised Segmentation

IEEE Signal Processing Letters (IF=3.9)

Abstract

         The generalization capability of models during inference in medical imaging, especially across data from different centers, is crucial, particularly in the context of limited data availability. Semi-supervised domain generalization learning has explored the use of unlabeled data to help the model generalize to unseen domains. However, existing methods primarily focus on decoupling and fusing information within the source distribution, resulting in limited improvement to the generalization ability. In contrast, we extend the exploration beyond source distributions and propose domain distribution boundary perturbation consistency learning for semi-supervised domain generalization in segmentation. We first use normalizing flows to map features to a mixture of Gaussian distributions, enabling the model to capture complex and diverse feature distributions across domains. Then, we perturb the features towards the domain boundaries and apply consistency regularization to maintain the quality of pseudo-labels while encouraging exploration of out-of-distribution features. This exploration aids the model’s adaptation to unseen domains and enhances generalization. Our method is simple and effective, outperforming existing state-of-the-art approaches on three public benchmark datasets and achieving optimal generalization performance.