Source-free domain adaptation (SFDA) alleviates the domain discrepancy among
data obtained from domains without accessing the data for the awareness of data
privacy. However, existing conventional SFDA methods face inherent limitations
in medical contexts, where medical data are typically collected from multiple
institutions using various equipment. To address this problem, we propose a
simple yet effective method, named Uncertainty-aware Adaptive Distillation
(UAD) for the multi-source-free unsupervised domain adaptation (MSFDA) setting.
UAD aims to perform well-calibrated knowledge distillation from (i) model level
to deliver coordinated and reliable base model initialisation and (ii) instance
level via model adaptation guided by high-quality pseudo-labels, thereby
obtaining a high-performance target domain model. To verify its general
applicability, we evaluate UAD on two image-based diagnosis benchmarks among
two multi-centre datasets, where our method shows a significant performance
gain compared with existing works. The code will be available soon.

提出一种名为 UAD 的简单而有效方法，用于医学领域中的多源免监督域适应，通过模型层和实例层的知识蒸馏，实现高性能的目标领域模型，验证了其在两个图像诊断基准数据集上的普适性和显著性能提升。