Black-Box Unsupervised Domain Adaptation for Medical Image Segmentation

Unsupervised Domain Adaptation (UDA) is one of the key technologies to solve the problem of obtaining ground truth labels needed for supervised learning. In general, UDA assumes that information about the source model, such as its architecture and weights, and all samples from the source domains are available when a target domain model is trained. However, this is not a realistic assumption in applications where privacy and white-box attacks are a concern, or where the model is only be accessible through an API. To overcome this limitation, UDA without source model information and source data, called Black-Box Unsupervised Domain Adaptation (BBUDA), has recently been proposed. Here, we propose an improved BBUDA method for medical image segmentation. Our main contribution is the introduction of a mean teacher algorithm during the training of the target domain model. We conduct experiments on datasets containing different types of source-target domain combinations to demonstrate the versatility and robustness of our method. We confirm that our method outperforms the state-of-the-art on all datasets.