Continual test-time adaptation via weight averaging of feature augmentations in cross-domain medical image segmentation.

Continual Test-Time Adaptation (CTTA) effectively mitigates domain shifts that occur across medical institutions by allowing models to adapt at test time without revisiting the source data. Existing CTTA methods exploit transform invariance through pixel-level augmentation, but these approaches tend to produce inconsistent results in online environments, particularly critical in semantic segmentation tasks. To address these issues, we propose FAWA, a CTTA framework that combines feature-based augmentation and a weighted averaging strategy. FAWA augments features by retrieving statistical information from a continuously updated memory bank, which generates diverse perturbations of input target samples. Each transformed sample is independently fine-tuned, and the resulting parameter sets are merged through continuous weighted averaging. This process leads the model to converge to a stable minimum and mitigates the accumulated error. Experiments on Brain CT and Prostate MRI benchmarks indicate that FAWA achieves competitive performance while ensuring robustness during extended adaptation streams.