Unsupervised domain adaptation for segmentation with black-box source model

Xiaofeng Liu; Chaehwa Yoo; Fangxu Xing; C.-C. Jay Kuo; Georges El Fakhri; Je-Won Kang; Jonghye Woo

doi:10.1117/12.2607895

4 April 2022 Unsupervised domain adaptation for segmentation with black-box source model

Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, C.-C. Jay Kuo, Georges El Fakhri, Je-Won Kang, Jonghye Woo

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203210 (2022) https://doi.org/10.1117/12.2607895
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, C.-C. Jay Kuo, Georges El Fakhri, Je-Won Kang, and Jonghye Woo "Unsupervised domain adaptation for segmentation with black-box source model", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203210 (4 April 2022); https://doi.org/10.1117/12.2607895

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