  Cross domain pulmonary nodule detection suffers from performance degradation
due to large shift of data distributions between the source and target domain.
Besides, considering the high cost of medical data annotation, it is often
assumed that the target images are unlabeled. Existing approaches have made
much progress for this unsupervised domain adaptation setting. However, this
setting is still rarely plausible in the medical application since the source
medical data are often not accessible due to the privacy concerns. This
motivates us to propose a Source-free Unsupervised cross-domain method for
Pulmonary nodule detection (SUP). It first adapts the source model to the
target domain by utilizing instance-level contrastive learning. Then the
adapted model is trained in a teacher-student interaction manner, and a
weighted entropy loss is incorporated to further improve the accuracy.
Extensive experiments by adapting a pre-trained source model to three popular
pulmonary nodule datasets demonstrate the effectiveness of our method.
