Ling Huang
Imperial, NUS, UTC, CNRS, Sorbonne
Dr. Ling HUANG is a Research Fellow at the National University of Singapore. Her research focuses on developing trustworthy AI solutions to ensure decision accuracy and reliability, as well as algorithm transparency and explainability. Her research focus on uncertainty quantification, multi-modality information fusion, and reliable and explainable decision-making with state-of-the-art deep models such as foundation models, LLM, etc. Her work was recognized by well-established journals, such as Information Fusion,Medical Image Analysis, and Int. Journal of Approximate Reasoning, and top international conferences, such as MICCAI and ISBI, BELIEF.
Publications
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Optimization of deep convolutional neural network for large scale image retrieval
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A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods
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Lymphoma segmentation from 3D PET-CT images using a deep evidential network
International Journal of Approximate Reasoning 149, 39-60, 2022L Huang, S Ruan, P Decazes, T Denoeux -
Saliency-based multi-feature modeling for semantic image retrieval
Journal of Visual Communication and Image Representation 50, 199-204, 2018C Bai, J Chen, L Huang, K Kpalma, S Chen -
Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey
Information Fusion 116, 102795, 2025Q Lin, Y Zhu, X Mei, L Huang, J Ma, K He, Z Peng, E Cambria, M Feng -
Unsupervised adversarial instance-level image retrieval
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Evidence fusion with contextual discounting for multi-modality medical image segmentation
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Application of belief functions to medical image segmentation: A review
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Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation
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Belief function-based semi-supervised learning for brain tumor segmentation
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Optimization of deep convolutional neural network for large scale image classification
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Covid-19 classification with deep neural network and belief functions
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Semi-supervised multiple evidence fusion for brain tumor segmentation
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Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
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Evidential segmentation of 3D PET/CT images
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Self-supervised quantized representation for seamlessly integrating knowledge graphs with large language models
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Adversarial learning for content-based image retrieval
2019 IEEE Conference on Multimedia Information Processing and Retrieval …, 2019L Huang, C Bai, Y Lu, S Chen, Q Tian -
An evidential time-to-event prediction model based on Gaussian random fuzzy numbers
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Unsupervised adversarial image retrieval
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Instance image retrieval with generative adversarial training
International Conference on Multimedia Modeling, 381-392, 2019H Li, C Bai, L Huang, Y Jiang, S Chen -
Evidential time-to-event prediction with calibrated uncertainty quantification
International Journal of Approximate Reasoning 181, 109403, 2025L Huang, Y Xing, S Mishra, T Denœux, M Feng -
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers
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Towards reliable medical image segmentation by utilizing evidential calibrated uncertainty
arXiv preprint arXiv:2301.00349, 2023K Zou, Y Chen, L Huang, X Yuan, X Shen, M Wang, RSM Goh, Y Liu, H Fu -
An evidence-based framework for heterogeneous electronic health records: A case study in mortality prediction
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A Foundation Model for Chest X-ray Interpretation with Grounded Reasoning via Online Reinforcement Learning
arXiv preprint arXiv:2509.03906, 2025Q Lin, Y Zhu, B Pu, L Huang, H Luo, J Ma, Z Peng, T Zhao, F Xu, J Zhang, ... -
Improving Clinical Foundation Models with Multi-modal Learning and Domain Adaptation for Chronic Disease Prediction
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Evidence-based multimodal fusion on structured EHRs and free-text notes for ICU outcome prediction
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EsurvFusion: An evidential multimodal survival fusion model based on Epistemic random fuzzy sets
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DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
arXiv preprint arXiv:2510.00053, 2025Y Xing, L Huang, J Ma, R Hong, J Qiu, P Liu, K He, H Fu, M Feng -
Toward Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
IEEE Transactions on Cybernetics, 2025K Zou, Y Chen, L Huang, N Zhou, X Yuan, X Shen, M Wang, RSM Goh, ... -
Domain-continual learning for multi-center anatomical detection via prompt-enhanced and densely-fused MedSAM
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Medical image segmentation with belief function theory and deep learning
Research Areas
Uncertainty Quantification Multimodal Information Fusion Dempster-Shafer theory Trustworthy AI healthcare Natural Language Processing Cancer survival analysis Cardivascual computing Gene-environment analysis
Scientific Activites
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Awards
- Best paper rewards (BELIEF 2024)
- 2024 SFGBM Thesis Prize
- Frontrunner 5000 -Top Articles from Outstanding S&T Journals of China (2021)
- Journal of Software High Impact Papers (2019 and 2020)
- Excellent graduation thesis of Zhejiang University of Technology (2019)
- Outstanding Graduates of Zhejiang Province (2019)
- Chinese National Scholarship (The highest honor for graduate students) (2017)
- Outstanding Graduates of Anhui University of Technology (2016)
- Turkey Mevlana Exchange student scholarship (2015)