Deep learning-based diagnostic systems have demonstrated potential in skin
disease diagnosis. However, their performance can easily degrade on test
domains due to distribution shifts caused by input-level corruptions, such as
imaging equipment variability, brightness changes, and image blur. This will
reduce the reliability of model deployment in real-world scenarios. Most
existing solutions focus on adapting the source model through retraining on
different target domains. Although effective, this retraining process is
sensitive to the amount of data and the hyperparameter configuration for
optimization. In this paper, we propose a test-time image adaptation method to
enhance the accuracy of the model on test data by simultaneously updating and
predicting test images. We modify the target test images by projecting them
back to the source domain using a diffusion model. Specifically, we design a
structure guidance module that adds refinement operations through low-pass
filtering during reverse sampling, regularizing the diffusion to preserve
structural information. Additionally, we introduce a self-ensembling scheme
automatically adjusts the reliance on adapted and unadapted inputs, enhancing
adaptation robustness by rejecting inappropriate generative modeling results.
To facilitate this study, we constructed the ISIC2019-C and Dermnet-C
corruption robustness evaluation benchmarks. Extensive experiments on the
proposed benchmarks demonstrate that our method makes the classifier more
robust across various corruptions, architectures, and data regimes. Our
datasets and code will be available at
https://github.com/minghu0830/Skin-TTA_Diffusion.

我们提出了一种测试时间图像适应方法，通过同时更新和预测测试图像来提高模型在测试数据上的准确性，通过扩散模型将目标测试图像反投影到源域，设计结构指导模块通过低通滤波添加细化操作，用于正则化扩散以保留结构信息，并引入自整合方案自动调整对适应和未适应输入的依赖关系，增强适应鲁棒性。在我们构建的 ISIC2019-C 和 Dermnet-C 损坏鲁棒性评估基准上进行的大量实验表明，我们的方法在各种损坏、体系结构和数据区域上使分类器更加鲁棒。我们的数据集和代码将在 https://github.com/minghu0830/Skin-TTA_Diffusion 上提供。