  Medical images from different healthcare centers exhibit varied data
distributions, posing significant challenges for adapting lung nodule detection
due to the domain shift between training and application phases. Traditional
unsupervised domain adaptive detection methods often struggle with this shift,
leading to suboptimal outcomes. To overcome these challenges, we introduce a
novel domain adaptive approach for lung nodule detection that leverages mean
teacher self-training and contrastive learning. First, we propose a
hierarchical contrastive learning strategy to refine nodule representations and
enhance the distinction between nodules and background. Second, we introduce a
nodule-level domain-invariant feature learning (NDL) module to capture
domain-invariant features through adversarial learning across different
domains. Additionally, we propose a new annotated dataset of X-ray images to
aid in advancing lung nodule detection research. Extensive experiments
conducted on multiple X-ray datasets demonstrate the efficacy of our approach
in mitigating domain shift impacts.
