标题： AIF-SFDA：用于医学图像分割的自主信息过滤驱动无源域适应 显示英文标题

标题： AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation

摘要： 从领域相关信息（DVI）中解耦领域无关信息（DII）是缓解深度学习算法在实际应用中领域偏移的一种显著策略。然而，在医疗环境中，数据收集和隐私方面的担忧通常会限制对训练和测试数据的访问，阻碍了现有方法对信息的实证解耦。为了解决这个问题，我们提出了一种自主信息过滤驱动的无源领域自适应（AIF-SFDA）算法，该算法利用基于频率的可学习信息过滤器来自主解耦DVI和DII。信息瓶颈（IB）和自监督（SS）被引入以优化可学习的频率过滤器。IB控制过滤器内的信息流以减少冗余的DVI，而SS则在与特定任务和图像模态一致的情况下保留DII。因此，自主信息过滤器可以仅依靠目标数据克服领域偏移。进行了一系列实验，涵盖多种医学图像模态和分割任务，通过与领先算法和消融研究的比较来展示AIF-SFDA的优势。代码可在 https://github.com/JingHuaMan/AIF-SFDA 获取。 显示英文摘要

摘要： Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms. However, in medical settings, concerns surrounding data collection and privacy often restrict access to both training and test data, hindering the empirical decoupling of information by existing methods. To tackle this issue, we propose an Autonomous Information Filter-driven Source-free Domain Adaptation (AIF-SFDA) algorithm, which leverages a frequency-based learnable information filter to autonomously decouple DVI and DII. Information Bottleneck (IB) and Self-supervision (SS) are incorporated to optimize the learnable frequency filter. The IB governs the information flow within the filter to diminish redundant DVI, while SS preserves DII in alignment with the specific task and image modality. Thus, the autonomous information filter can overcome domain shifts relying solely on target data. A series of experiments covering various medical image modalities and segmentation tasks were conducted to demonstrate the benefits of AIF-SFDA through comparisons with leading algorithms and ablation studies. The code is available at https://github.com/JingHuaMan/AIF-SFDA.