Cross Domain Pulmonary Nodule Detection Without Source Data

The model performance on cross-domain pulmonary nodule detection usually degrades because of the significant shift in data distributions and the scarcity of annotated medical data in the test scenarios. Current approaches to cross-domain object detection assume that training data from the source domain are freely available; however, such an assumption is implausible in the medical field, as the data are confidential and cannot be shared due to privacy concerns. Thus, this paper introduces source data-free cross-domain pulmonary nodule detection. In this setting, only a pre-trained model from the source domain and a few annotated samples from the target domain are available. We introduce a novel method to tackle this issue, adapting the feature extraction module for the target domain through minimizing the proposed General Entropy (GE). Specifically, we optimize the batch normalization (BN) layers of the model by GE minimization. Thus, the dataset-level statistics of the target domain are utilized for optimization and inference. Furthermore, we tune the detection head of the model using annotated target samples to mitigate the rater difference and improve the accuracy. Extensive experiments on three different pulmonary nodule datasets show the efficacy of our method for source data-absent cross-domain pulmonary nodule detection.