AIF-SFDA：面向医学图像分割的自主信息滤波器驱动的无源域域适应
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation
AI总结：
针对医学图像分割中源数据不可访问导致的域偏移问题，提出AIF-SFDA，利用IB与自监督优化的频率信息滤波器，仅用目标数据解耦DVI和DII。
摘要：
将域变化信息（DVI）与域不变信息（DII）解耦，是在深度学习算法实际部署中缓解域偏移的一种重要策略。然而，在医疗场景中，围绕数据收集和隐私的担忧往往限制了对训练数据和测试数据的访问，阻碍了现有方法对信息进行经验性解耦。为解决这一问题，我们提出了一种自主信息滤波器驱动的无源域域适应（AIF-SFDA）算法，该算法利用基于频率的可学习信息滤波器来自主解耦DVI和DII。该方法结合信息瓶颈（IB）和自监督（SS）来优化可学习频率滤波器。IB控制滤波器内的信息流，以减少冗余的DVI，而SS则根据具体任务和图像模态保留DII。因此，该自主信息滤波器能够仅依靠目标数据克服域偏移。研究开展了一系列涵盖多种医学图像模态和分割任务的实验，通过与领先算法比较以及消融研究证明了AIF-SFDA的优势。代码可在https://github.com/JingHuaMan/AIF-SFDA获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
