Sample-aware test-time adaptation for medical image-to-image translation

Image-to-image translation has emerged as a powerful technique in medical imaging applications, including image denoising and cross-modality synthesis. However, pretrained translation models may exhibit performance degradation when test samples differ from the source-domain data observed during training. We propose a sample-aware Test-Time Adaptation framework that dynamically determines whether and how each test sample should be adapted. The framework combines a Reconstruction Module, which measures compatibility with source-domain representations, with a Dynamic Adaptation Block that selectively modifies the internal feature levels of a frozen translation model. The output reconstruction error is used to trigger adaptation and to guide the sample-specific selection and optimization of lightweight feature-level adaptors. We evaluate the proposed approach on low-dose CT denoising and T 1 -to- T 2 MRI translation under source-domain, anatomical-shift, and external-domain conditions. The proposed method improves translation quality under anatomical and external-domain shifts, while remaining comparable to the frozen task model in the predominantly in-distribution setting, where adaptation provides no measurable benefit. Our results show that combining selective activation with sample-specific adaptor selection provides a more reliable strategy than applying a fixed adaptation procedure uniformly across test samples. The code is available at: https://github.com/arco-group/Sample-Aware-TTA .