  Medical imaging provides many valuable clues involving anatomical structure
and pathological characteristics. However, image degradation is a common issue
in clinical practice, which can adversely impact the observation and diagnosis
by physicians and algorithms. Although extensive enhancement models have been
developed, these models require a well pre-training before deployment, while
failing to take advantage of the potential value of inference data after
deployment. In this paper, we raise an algorithm for source-free unsupervised
domain adaptive medical image enhancement (SAME), which adapts and optimizes
enhancement models using test data in the inference phase. A
structure-preserving enhancement network is first constructed to learn a robust
source model from synthesized training data. Then a teacher-student model is
initialized with the source model and conducts source-free unsupervised domain
adaptation (SFUDA) by knowledge distillation with the test data. Additionally,
a pseudo-label picker is developed to boost the knowledge distillation of
enhancement tasks. Experiments were implemented on ten datasets from three
medical image modalities to validate the advantage of the proposed algorithm,
and setting analysis and ablation studies were also carried out to interpret
the effectiveness of SAME. The remarkable enhancement performance and benefits
for downstream tasks demonstrate the potential and generalizability of SAME.
The code is available at
https://github.com/liamheng/Annotation-free-Medical-Image-Enhancement.
