在跨域医学图像分割中，通过特征增强的加权平均实现连续的测试时间自适应

摘要(中文译文)

持续测试时间适应（CTTA）通过允许模型在测试时进行适应而无需重新访问源数据，有效缓解了跨医疗机构发生的域偏移。现有的CTTA方法通过像素级增强来利用变换不变性，但这些方法在在线环境中往往产生不一致的结果，这在语义分割任务中尤为关键。为解决这些问题，我们提出了FAWA，一种结合基于特征的增强与加权平均策略的CTTA框架。FAWA通过从持续更新的记忆库中检索统计信息来增强特征，从而对输入目标样本生成多样化的扰动。每个变换后的样本被独立微调，所得参数集通过连续加权平均进行合并。该过程引导模型收敛至稳定最小值并减轻累积误差。在脑部CT和前列腺MRI基准上的实验表明，FAWA在确保扩展适应流期间鲁棒性的同时，取得了具有竞争力的性能。

Abstract

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.

如何引用