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.

翻译：针对医学图像分析的深度学习技术通常受困于源域与目标域之间的域偏移。现有研究主要聚焦于无监督域自适应（UDA）。然而在实际应用中，隐私问题更为严峻。例如，不同医院的数据因设备差异存在域偏移，且由于隐私保护，两个域的数据无法同时获取。在此定义为"无源UDA"的挑战下，以往基于UDA的医学方法受到限制。尽管已有多种医学无源无监督域自适应（MSFUDA）方法被提出，但我们发现这些方法会陷入一种过拟合困境——"训练时间越长，性能越差"。为此，我们提出稳定学习（SL）策略以解决这一困境。SL是一种可扩展方法，能够与其他研究工作集成，其核心包含权重巩固与熵增两个模块：首先通过权重巩固保留域不变知识，随后设计熵增机制避免过度学习。对比实验证明了SL的有效性，并进行了全面的消融实验。此外，我们将开源包含多种MSFUDA方法的代码库。