标题： F^2TTA：通过图像级解耦提示微调在跨域医学图像分类中的自由形式测试时适应 显示英文标题

标题： F^2TTA: Free-Form Test-Time Adaptation on Cross-Domain Medical Image Classification via Image-Level Disentangled Prompt Tuning

摘要： 测试时适应（TTA）已成为一种有前景的解决方案，利用未标记的测试数据将源模型适应到未见过的医学站点，因为数据标注的成本很高。 现有的TTA方法考虑了数据从一个或多个域以完整的域单元到达的情况。 然而，在临床实践中，由于资源限制和患者变异性，数据通常以任意长度的域片段和随机到达顺序的形式出现。 本文研究了一个实际的自由形式测试时适应（F$^{2}$TTA）任务，其中源模型被适应到这种自由形式的域片段，且片段之间可能发生不可预测的分布偏移。 在这种情况下，这些偏移可能会扭曲适应过程。 为了解决这个问题，我们提出了一种新的图像级解耦提示调优（I-DiPT）框架。 I-DiPT使用图像不变提示，探索域不变表示以减轻不可预测的偏移，并使用图像特定提示将源模型适应到来自传入片段的每个测试图像。 由于训练时仅有一个图像可用，提示可能面临知识表示不足的问题。 为了克服这一限制，我们首先引入了面向不确定性的掩码（UoM），通过由源模型表示不确定性驱动的掩码一致性学习，鼓励提示从传入图像中提取足够的信息。 然后，我们进一步提出了一种并行图蒸馏（PGD）方法，通过并行图网络重新利用历史图像特定和图像不变提示中的知识。 在乳腺癌和青光眼分类上的实验表明，我们的方法在F$^{2}$TTA方面优于现有的TTA方法。 代码可在https://github.com/mar-cry/F2TTA获取。 显示英文摘要

摘要： Test-Time Adaptation (TTA) has emerged as a promising solution for adapting a source model to unseen medical sites using unlabeled test data, due to the high cost of data annotation. Existing TTA methods consider scenarios where data from one or multiple domains arrives in complete domain units. However, in clinical practice, data usually arrives in domain fragments of arbitrary lengths and in random arrival orders, due to resource constraints and patient variability. This paper investigates a practical Free-Form Test-Time Adaptation (F$^{2}$TTA) task, where a source model is adapted to such free-form domain fragments, with shifts occurring between fragments unpredictably. In this setting, these shifts could distort the adaptation process. To address this problem, we propose a novel Image-level Disentangled Prompt Tuning (I-DiPT) framework. I-DiPT employs an image-invariant prompt to explore domain-invariant representations for mitigating the unpredictable shifts, and an image-specific prompt to adapt the source model to each test image from the incoming fragments. The prompts may suffer from insufficient knowledge representation since only one image is available for training. To overcome this limitation, we first introduce Uncertainty-oriented Masking (UoM), which encourages the prompts to extract sufficient information from the incoming image via masked consistency learning driven by the uncertainty of the source model representations. Then, we further propose a Parallel Graph Distillation (PGD) method that reuses knowledge from historical image-specific and image-invariant prompts through parallel graph networks. Experiments on breast cancer and glaucoma classification demonstrate the superiority of our method over existing TTA approaches in F$^{2}$TTA. Code is available at https://github.com/mar-cry/F2TTA.