无需源数据，自动分离医学影像中的域不变与域变化信息。
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation
- 用可学习的频域滤波器自动分离图像中的域不变与域变化特征。
- 仅用目标数据训练，在多个医学影像分割任务上达到领先性能。
- 适合隐私敏感的医疗场景，尤其适用于无法获取源数据的部署环境。
将域变化信息（DVI）与域不变信息（DII）解耦是缓解深度学习中域偏移问题的有效策略。然而在医疗领域，数据采集和隐私保护限制了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。为此，我们提出自主信息滤波驱动的无源域适应算法（AIF-SFDA），利用基于频率的可学习信息滤波器，实现DVI与DII的自主解耦。引入信息瓶颈（IB）和自监督（SS）优化可学习滤波器：IB控制滤波器内的信息流以减少冗余的DVI，SS则保留与特定任务及影像模态一致的DII。因此，该自主信息滤波器仅依赖目标数据即可克服域偏移。通过覆盖多种医学影像模态和分割任务的实验，结合主流算法对比与消融研究，验证了AIF-SFDA的优势。代码已开源：https://github.com/JingHuaMan/AIF-SFDA。
原文摘要 · Abstract (English)
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.
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