针对医学图像翻译中的异常样本，动态调整模型处理方式以提升鲁棒性。
Sample-Aware Test-Time Adaptation for Medical Image-to-Image Translation
- 根据测试样本特征动态调整翻译过程，区分是否需要适应。
- 在低剂量CT去噪和T1到T2 MRI转换任务上均优于基线和现有TTA方法。
- 适合需应对真实世界分布偏移的医学图像翻译场景。
图像到图像翻译在医学成像中广泛应用，如图像去噪和跨模态转换，但对分布外样本表现不佳。为此，我们提出一种新型测试时自适应（TTA）框架，基于每个测试样本的特性动态调整翻译过程。方法引入重建模块量化领域偏移，并设计动态自适应模块，仅对需调整的样本修改预训练模型内部特征，避免对分布内样本性能的影响。在低剂量CT去噪和T1到T2 MRI转换两个任务上评估，结果一致优于无TTA的基线模型及现有TTA方法。分析表明，现有最优方法对分布内外样本统一应用适应存在缺陷，而样本级动态调整显著提升模型在真实场景下的鲁棒性。代码已开源：https://github.com/Sample-Aware-TTA/Code。
原文摘要 · Abstract (English)
Image-to-image translation has emerged as a powerful technique in medical imaging, enabling tasks such as image denoising and cross-modality conversion. However, it suffers from limitations in handling out-of-distribution samples without causing performance degradation. To address this limitation, we propose a novel Test-Time Adaptation (TTA) framework that dynamically adjusts the translation process based on the characteristics of each test sample. Our method introduces a Reconstruction Module to quantify the domain shift and a Dynamic Adaptation Block that selectively modifies the internal features of a pretrained translation model to mitigate the shift without compromising the performance on in-distribution samples that do not require adaptation. We evaluate our approach on two medical image-to-image translation tasks: low-dose CT denoising and T1 to T2 MRI translation, showing consistent improvements over both the baseline translation model without TTA and prior TTA methods. Our analysis highlights the limitations of the state-of-the-art that uniformly apply the adaptation to both out-of-distribution and in-distribution samples, demonstrating that dynamic, sample-specific adjustment offers a promising path to improve model resilience in real-world scenarios. The code is available at: https://github.com/Sample-Aware-TTA/Code.
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