Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H. and Chandraker, M. (2018) Learning to Adapt Structured Output Space for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 7472-7481.
https://doi.org/10.1109/CVPR.2018.00780

has been cited by the following article:

• TITLE: Semantic Constraint Based Unsupervised Domain Adaptation for Cardiac Segmentation

  AUTHORS: Xin Wang, Fan Zhu, Yaxin Peng, Chaomin Shen, Zhen Ye, Chaozheng Zhou

  KEYWORDS: Medical Image Segmentation, Domain Adaptation, Category-Wise Alignment, Cardiac Segmentation

  JOURNAL NAME: Advances in Pure Mathematics, Vol.11 No.6, June 30, 2021

  ABSTRACT: The segmentation of unlabeled medical images is troublesome due to the high cost of annotation, and unsupervised domain adaptation is one solution to this. In this paper, an improved unsupervised domain adaptation method was proposed. The proposed method considered both global alignment and category-wise alignment. First, we aligned the appearance of two domains by image transformation. Second, we aligned the output maps of two domains in a global way. Then, we decomposed the semantic prediction map by category, aligning the prediction maps in a category-wise manner. Finally, we evaluated the proposed method on the 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, and obtained 82.1 on the dice similarity coefficient and 4.6 on the average symmetric surface distance, demonstrating the effectiveness of the combination of global alignment and category-wise alignment.