ArticleProceedings of SPIE--the International Society for Optical Engineering
Unsupervised Domain Adaptation for Segmentation with Black-box Source Model.
Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Source-free unsupervised domain adaptation: A survey.Neural networks : the official journal of the International Neural Network Society · 2024Pooled it
- Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.Frontiers in neuroscience · 2024Pooled it
- Variance Extrapolated Class-Imbalance-Aware Domain Adaptive Myocardial Segmentation in Multi-Sequence Cardiac MRI.IEEE journal of biomedical and health informatics · 2026Article
- Light-weight Fine-tuning Method for Defending Adversarial Noise in Pre-trained Medical Vision-Language Models.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Memory consistent unsupervised off-the-shelf model adaptation for source-relaxed medical image segmentation.Medical image analysis · 2023Article
- SELF-SEMANTIC CONTOUR ADAPTATION FOR CROSS MODALITY BRAIN TUMOR SEGMENTATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
- Unsupervised Black-Box Model Domain Adaptation for Brain Tumor Segmentation.Frontiers in neuroscience · 2022Article
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7 authors.
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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.
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