Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90946
Title: | Unsupervised domain adaptation network with category-centric prototype aligner for biomedical image segmentation | Authors: | Gong, P Yu, W Sun, Q Zhao, R Hu, J |
Issue Date: | 2021 | Source: | IEEE access, 2021, v. 9, 9367129, p. 36500-36511 | Abstract: | With the widespread success of deep learning in biomedical image segmentation, domain shift becomes a critical and challenging problem, as the gap between two domains can severely affect model performance when deployed to unseen data with heterogeneous features. To alleviate this problem, we present a novel unsupervised domain adaptation network, for generalizing models learned from the labeled source domain to the unlabeled target domain for cross-modality biomedical image segmentation. Specifically, our approach consists of two key modules, a conditional domain discriminator (CDD) and a category-centric prototype aligner (CCPA). The CDD, extended from conditional domain adversarial networks in classifier tasks, is effective and robust in handling complex cross-modality biomedical images. The CCPA, improved from the graph-induced prototype alignment mechanism in cross-domain object detection, can exploit precise instance-level features through an elaborate prototype representation. In addition, it can address the negative effect of class imbalance via entropy-based loss. Extensive experiments on a public benchmark for the cardiac substructure segmentation task demonstrate that our method significantly improves performance on the target domain. | Keywords: | Biomedical image segmentation Category-centric prototype aligner Cross-modality learning Unsupervised domain adaptation |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | EISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2021.3063634 | Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ The following publication Gong, P., Yu, W., Sun, Q., Zhao, R., & Hu, J. (2021). Unsupervised Domain Adaptation Network With Category-Centric Prototype Aligner for Biomedical Image Segmentation. IEEE Access, 9, 36500-36511 is available at https://doi.org/10.1109/ACCESS.2021.3063634 |
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Unsupervised_Domain_Adaptation_Network_With_Category-Centric_Prototype_Aligner_for_Biomedical_Image_Segmentation.pdf | 6.84 MB | Adobe PDF | View/Open |
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