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.

翻译：跨域肺结节检测因源域与目标域数据分布存在较大偏移而面临性能下降问题。此外，考虑到医学数据标注的高昂成本，通常假设目标图像为无标签数据。现有方法在无监督领域自适应设置下已取得显著进展，然而该设置在医学应用中仍难以实现——由于隐私保护问题，源域医学数据往往无法获取。为此，我们提出一种无源无监督跨域肺结节检测方法（SUP）。该方法首先通过实例级对比学习将源模型适配至目标域，随后采用师生交互方式训练适配模型，并引入加权熵损失以进一步提升检测精度。通过在预训练源模型上对三个主流肺结节数据集进行跨域适配实验，充分验证了本方法的有效性。