Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data. In medical imaging scenarios, the practical significance of SFDA methods has been emphasized due to privacy concerns. Recent State-of-the-art SFDA methods primarily rely on self-training based on pseudo-labels (PLs). Unfortunately, PLs suffer from accuracy deterioration caused by domain shift, and thus limit the effectiveness of the adaptation process. To address this issue, we propose a Chebyshev confidence guided SFDA framework to accurately assess the reliability of PLs and generate self-improving PLs for self-training. The Chebyshev confidence is estimated by calculating probability lower bound of the PL confidence, given the prediction and the corresponding uncertainty. Leveraging the Chebyshev confidence, we introduce two confidence-guided denoising methods: direct denoising and prototypical denoising. Additionally, we propose a novel teacher-student joint training scheme (TJTS) that incorporates a confidence weighting module to improve PLs iteratively. The TJTS, in collaboration with the denoising methods, effectively prevents the propagation of noise and enhances the accuracy of PLs. Extensive experiments in diverse domain scenarios validate the effectiveness of our proposed framework and establish its superiority over state-of-the-art SFDA methods. Our paper contributes to the field of SFDA by providing a novel approach for precisely estimating the reliability of pseudo-labels and a framework for obtaining high-quality PLs, resulting in improved adaptation performance.

翻译：无源域适应（SFDA）旨在使在标记源域上训练的模型适应未标记目标域，而无需访问源数据。在医学影像场景中，由于隐私问题，SFDA方法的实际意义已得到强调。当前最先进的SFDA方法主要依赖基于伪标签（PLs）的自训练。然而，伪标签因域偏移导致准确率下降，从而限制了适应过程的有效性。为解决此问题，我们提出一种切比雪夫置信度引导的SFDA框架，以准确评估伪标签的可靠性并生成自改进的伪标签用于自训练。切比雪夫置信度通过计算预测及相应不确定性下伪标签置信度的概率下界来估计。基于切比雪夫置信度，我们引入两种置信度引导的去噪方法：直接去噪和原型去噪。此外，我们提出一种新颖的教师-学生联合训练方案（TJTS），该方案包含置信度加权模块以迭代改善伪标签。TJTS与去噪方法协同作用，有效抑制噪声传播并提升伪标签的准确性。在多种域场景下的广泛实验验证了我们提出框架的有效性，并证明其优于最先进的SFDA方法。本文通过提供精确估计伪标签可靠性的新方法及获得高质量伪标签的框架，为SFDA领域做出了贡献，从而提升了适应性能。