Unsupervised domain adaptation (UDA) has been widely used to transfer
knowledge from a labeled source domain to an unlabeled target domain to counter
the difficulty of labeling in a new domain. The training of conventional
solutions usually relies on the existence of both source and target domain
data. However, privacy of the large-scale and well-labeled data in the source
domain and trained model parameters can become the major concern of cross
center/domain collaborations. In this work, to address this, we propose a
practical solution to UDA for segmentation with a black-box segmentation model
trained in the source domain only, rather than original source data or a
white-box source model. Specifically, we resort to a knowledge distillation
scheme with exponential mixup decay (EMD) to gradually learn target-specific
representations. In addition, unsupervised entropy minimization is further
applied to regularization of the target domain confidence. We evaluated our
framework on the BraTS 2018 database, achieving performance on par with
white-box source model adaptation approaches.

本文提出了一种基于黑盒分割模型的无监督域自适应解决方案，利用指数混合衰减的知识蒸馏方案逐步学习目标特定表示，并应用无监督熵最小化来规范目标域置信度，最终在 BraTS 2018 数据库上取得了与白盒源模型适应方法相当的性能。