Email huang.l@nus.edu.sg

Huang Ling is now a research fellow in NUS Saw Swee Hock School of Public Health interested. She is interested in trust and explainable AI with the application of deep learning and evidence theory for imperfect (uncertain and imprecise) medical data analysis.

• NUS Saw Swee Hock School of Public Health

• Trusted and explainable Biomedical AI, Uncertainty modelling, evidential reasoning, information fusion, machine learning, deep learning

• Ph.D , Université de Technologie de Compiègne, France, 2019-2023
• M.Sc. Zhejiang Universite of Technology, China. 2016-2019
• Bachelor. Anhui University of Technology, China. 2024-2016

• Ling Huang, Su Ruan, and Thierry Denœux. “Application of belief functions to medical image segmentation: A review.” Information Fusion(2022).
• Ling Huang, et al. “Lymphoma segmentation from 3D PET-CT images using a deep evidential network.” International Journal of Approximate Reasoning149 (2022): 39-60.
• Ling Huang, et al. “Evidence fusion with contextual discounting for multi-modality medical image segmentation.” Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part V. Cham: Springer Nature Switzerland, 2022.
• Ling Huang, Su Ruan, and Thierry Denœux. “Semi-supervised multiple evidence fusion for brain tumor segmentation.” Neurocomputing535 (2023): 40-52.
• Ling Huang, Su Ruan, and Thierry Denoeux. “Belief function-based semi-supervised learning for brain tumor segmentation.” 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). IEEE, 2021.
• Ling Huang, et al. “Evidential segmentation of 3D PET/CT images.” Belief Functions: Theory and Applications: 6th International Conference, BELIEF 2021, Shanghai, China, October 15–19, 2021, Proceedings. Cham: Springer International Publishing, 2021.
• Ling Huang, Su Ruan, and Thierry Denoeux. “Covid-19 classification with deep neural network and belief functions.” The Fifth International Conference on Biological Information and Biomedical Engineering. 2021.
• Bai, C., Huang, L., Pan, X., Zheng, J., & Chen, S. (2018). Optimization of deep convolutional neural network for large scale image retrieval. Neurocomputing, 303, 60-67.