Unsupervised Cross-domain Pulmonary Nodule Detection without Source Data

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.