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Yanda Meng

Assistant Professor, Bioengineering

Biological and Environmental Science and Engineering Division
Email icon yanda.meng@outlook.com

Program Affiliations

Research Interests

Yanda is working at the intersection of artificial intelligence (AI), computer vision, and healthcare. His research focuses on developing advanced machine learning and biomedical image analysis techniques to tackle critical challenges in medicine and global health. Yanda’s research interests span medical image analysis, multimodality data learning, language-vision models, data-efficient deep learning, and trustworthy AI in healthcare. He has a particular focus on foundation models for medicine, exploring how large-scale AI systems can be adapted and personalised for clinical use while ensuring fairness, robustness, and interpretability. His long-term goal is to develop AI methods that are equitable, reliable, and impactful for patient care globally.

Education Profile

  • Postdoctoral Fellow, Department of Eye and Vision Science, University of Liverpool, Liverpool, UK (2024)

  • Ph.D., Department of Eye and Vision Science, University of Liverpool, Liverpool, UK (2022)

  • M.Sc., School of Computing, University of Leeds, Leeds, UK (2018)

  • B.Eng., School of Software Engineering, Capital Normal University, Beijing, China (2017)

Publications

  • Meng, Y., Zhang, Y., Xie, J., Duan, J., Joddrell, M., Madhusudhan, S., Peto, T., Zhao, Y. and Zheng,
    Y., 2024. Multi-granularity learning of explicit geometric constraint and contrast for label-efficient
    medical image segmentation and differentiable clinical function assessment. Medical Image
    Analysis, 95, p.103183.

  • Meng, Y., Bridge, J., Addison, C., Wang, M., Merritt, C., Franks, S., Mackey, M., Messenger, S., Sun,
    R., Fitzmaurice, T. and McCann, C., 2023. Bilateral adaptive graph convolutional network on CT
    based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance
    learning. Medical Image Analysis, 84, p.102722.

  • Meng, Y., Zhang, H., Zhao, Y., Gao, D., Hamill, B., Patri, G., Peto, T., Madhusudhan, S. and Zheng,
    Y., 2022. Dual consistency enabled weakly and semi-supervised optic disc and cup segmentation
    with dual adaptive graph convolutional networks. IEEE Transactions on Medical Imaging, 42(2),
    pp.416-429.

  • Meng, Y., Zhang, H., Zhao, Y., Yang, X., Qiao, Y., MacCormick, I.J., Huang, X. and Zheng, Y., 2021.
    Graph-based region and boundary aggregation for biomedical image segmentation. IEEE
    Transactions on Medical Imaging, 41(3), pp.690-701.