@InProceedings{10.1007/978-981-97-8496-7_36, author="Wang, Liwen and Zhang, Xiaoyan and He, Guannan and Tan, Ying and Li, Shengli and Pu, Bin and Jin, Zhe and Sha, Wen and Dong, Xingbo",
                    editor="Lin, Zhouchen and Cheng, Ming-Ming and He, Ran and Ubul, Kurban and Silamu, Wushouer and Zha, Hongbin and Zhou, Jie and Liu, Cheng-Lin",
                    title="Learning Frequency and Structure in UDA for Medical Object Detection",
                    booktitle="Pattern Recognition and Computer Vision",
                    year="2025",
                    publisher="Springer Nature Singapore",
                    address="Singapore",
                    pages="518--532",
                    abstract="In medical imaging applications, particularly in cardiac and skeletal analysis, the anatomical structure detection is crucial for diagnosing cardiac disease and other disease. However, the domain gap between images acquired from different sources or modalities poses a significant challenge and impedes model generalization across diverse patient populations and imaging conditions. Bridging this gap is particularly essential in image-based diagnosis, where subtle variations in anatomical structures and imaging characteristics can profoundly impact diagnostic performance. Take fetal cardiac ultrasound images as an example, this paper proposes a novel method for unsupervised domain adaptive fetal cardiac structure detection. The method integrates both the frequency-based distributional properties and anatomical structural information inherent in medical images. Specifically, we introduce a Frequency Distribution Alignment (FDA) module and an Organ Structure Alignment (OSA) module to mitigate detection misalignment across different hospital settings. We demonstrates the effectiveness of these modules through extensive experiments. Our method significantly improves the performance of fetal cardiac structure detection tasks, enabling adaptation to diverse hospital scenarios and showcasing its potential in addressing domain gaps in medical imaging.",
                    isbn="978-981-97-8496-7"
                    }

@inproceedings{zhang2024validating,
                    title={Validating Privacy-Preserving Face Recognition under a Minimum Assumption},
                    author={Zhang, Hui and Dong, Xingbo and Lai, YenLung and Zhou, Ying and Zhang, Xiaoyan and Lv, Xingguo and Jin, Zhe and Li, Xuejun},
                    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
                    pages={12205--12214},
                    year={2024},
                    publisher={CVPR}
                  }