Distribution shift is a common challenge in medical images obtained from different clinical centers, significantly hindering the deployment of pre-trained semantic segmentation models in real-world applications across multiple domains. Continual Test-Time Adaptation(CTTA) has emerged as a promising approach to address cross-domain shifts during continually evolving target domains. Most existing CTTA methods rely on incrementally updating model parameters, which inevitably suffer from error accumulation and catastrophic forgetting, especially in long-term adaptation. Recent prompt-tuning-based works have shown potential to mitigate the two issues above by updating only visual prompts. While these approaches have demonstrated promising performance, several limitations remain:1)lacking multi-scale prompt diversity, 2)inadequate incorporation of instance-specific knowledge, and 3)risk of privacy leakage. To overcome these limitations, we propose Multi-scale Global-Instance Prompt Tuning(MGIPT), to enhance scale diversity of prompts and capture both global- and instance-level knowledge for robust CTTA. Specifically, MGIPT consists of an Adaptive-scale Instance Prompt(AIP) and a Multi-scale Global-level Prompt(MGP). AIP dynamically learns lightweight and instance-specific prompts to mitigate error accumulation with adaptive optimal-scale selection mechanism. MGP captures domain-level knowledge across different scales to ensure robust adaptation with anti-forgetting capabilities. These complementary components are combined through a weighted ensemble approach, enabling effective dual-level adaptation that integrates both global and local information. Extensive experiments on medical image segmentation benchmarks demonstrate that our MGIPT outperforms state-of-the-art methods, achieving robust adaptation across continually changing target domains.

翻译：分布偏移是来自不同临床中心的医学图像中常见的挑战，显著阻碍了预训练语义分割模型在跨多个领域的实际应用中的部署。持续测试时适应(CTTA)已成为一种有前景的方法，用于在不断演化的目标域中应对跨域偏移。大多数现有CTTA方法依赖于增量更新模型参数，这不可避免地会遭受误差累积和灾难性遗忘问题，尤其是在长期适应过程中。最近基于提示调优的研究通过仅更新视觉提示，显示出缓解上述两个问题的潜力。尽管这些方法已展现出良好的性能，但仍存在若干局限性：1)缺乏多尺度提示多样性，2)对实例特定知识的整合不足，以及3)隐私泄露风险。为克服这些局限，我们提出多尺度全局-实例提示调优(MGIPT)，以增强提示的尺度多样性，并同时捕获全局和实例级知识，实现鲁棒的CTTA。具体而言，MGIPT包含自适应尺度实例提示(AIP)和多尺度全局级提示(MGP)。AIP通过自适应最优尺度选择机制，动态学习轻量级且实例特定的提示以减轻误差累积。MGP在不同尺度上捕获域级知识，确保具有抗遗忘能力的鲁棒适应。这些互补组件通过加权集成方法相结合，实现了整合全局与局部信息的有效双级适应。在医学图像分割基准测试上的大量实验表明，我们的MGIPT优于现有最先进方法，在不断变化的目标域中实现了鲁棒的适应。