Deep learning techniques for medical image analysis usually suffer from the domain shift between source and target data. Most existing works focus on unsupervised domain adaptation (UDA). However, in practical applications, privacy issues are much more severe. For example, the data of different hospitals have domain shifts due to equipment problems, and data of the two domains cannot be available simultaneously because of privacy. In this challenge defined as Source-Free UDA, the previous UDA medical methods are limited. Although a variety of medical source-free unsupervised domain adaption (MSFUDA) methods have been proposed, we found they fall into an over-fitting dilemma called "longer training, worse performance." Therefore, we propose the Stable Learning (SL) strategy to address the dilemma. SL is a scalable method and can be integrated with other research, which consists of Weight Consolidation and Entropy Increase. First, we apply Weight Consolidation to retain domain-invariant knowledge and then we design Entropy Increase to avoid over-learning. Comparative experiments prove the effectiveness of SL. We also have done extensive ablation experiments. Besides, We will release codes including a variety of MSFUDA methods.

医学图像分析中的深度学习技术通常受到源数据和目标数据之间的领域转移问题的困扰。现有的工作主要集中在无监督域自适应（UDA）上，但在实际应用中，隐私问题更为严重。本研究针对源自由无监督域自适应（Source-Free UDA）的挑战，提出了稳定学习（Stable Learning）策略，该策略以权重一致性和熵增加为核心来解决目前UDA医学方法所面临的过拟合困境。实验证明了稳定学习的有效性，并进行了大量消融实验。此外，研究团队还将发布包含多种MSFUDA方法的代码。

SL: 医学图像分割中的无源域自适应稳定学习