The Devil is in the Frequency: Constrained and Adaptive Fine-Grained Domain Perturbation for Robust Medical Segmentation

Domain generalization (DG) in medical image analysis is critical for achieving consistent and reliable diagnostics across diverse healthcare systems. However, domain shifts resulting from variations in imaging protocols, devices, and practices hinder accurate anatomical identification. While data augmentation shows promise, it struggles to generate diverse samples that bridge domain gaps and often distorts invariant anatomical features, compromising diagnostic integrity. This paper introduces the Adaptive Dual-Space Spectral Perturbation (AdaDSP) framework to address these issues at both broad and fine-grained levels. At the broad level, AdaDSP injects learnable spectral perturbations into input images and intermediate feature maps, significantly enhancing the diversity of the training data. At the fine-grained level, we propose a Fine-Grained Spectral Perturbation module that utilizes two lightweight attention mechanisms to capture sensitive frequency bands that hinder generalization. By injecting multivariate Gaussian noise within a mini-batch, this module better modulates the distribution of frequencies and accomplishes adaptive perturbation of sensitive frequency bands. Furthermore, we introduce a Universal Triple-stage Semantic Constraint Framework to encourage the networks to learn domain-invariant representations while retaining the discriminabtive capacity. Extensive experiments show that our method outperforms state-of-the-art benchmarks, with improvements of 2.40% and 2.99% in two notable medical imaging tasks, respectively.