@inproceedings{24dbe553e315482d9d134a4c3a4a1f17,
title = "Unsupervised Domain Adaptation for Segmentation with Black-box Source Model",
abstract = "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.",
keywords = "Black-box source model, Brain MR image segmentation, Unsupervised domain adaptation",
author = "Xiaofeng Liu and Chaehwa Yoo and Fangxu Xing and Kuo, {C. C.Jay} and {El Fakhri}, Georges and Kang, {Je Won} and Jonghye Woo",
note = "Funding Information: This work is partially supported by NIH R01DC018511 and P41EB022544. Publisher Copyright: {\textcopyright} 2022 SPIE.; null ; Conference date: 21-03-2021 Through 27-03-2021",
year = "2022",
doi = "10.1117/12.2607895",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Olivier Colliot and Ivana Isgum and Landman, {Bennett A.} and Loew, {Murray H.}",
booktitle = "Medical Imaging 2022",
}