  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.
