Ling Huang

Orcid: 0000-0003-1562-1371

Affiliations:
  • National University of Singapore, School of Public Health, Singapore
  • University of Technology of Compiègne, France (former)


According to our database1, Ling Huang authored at least 20 papers between 2021 and 2025.

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Bibliography

2025
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models.
CoRR, January, 2025

Evidence-based multimodal fusion on structured EHRs and free-text notes for ICU outcome prediction.
CoRR, January, 2025

Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey.
Inf. Fusion, 2025

Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation.
Inf. Fusion, 2025

2024
A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods.
Medical Image Anal., 2024

EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers.
CoRR, 2024

Evidential time-to-event prediction model with well-calibrated uncertainty estimation.
CoRR, 2024

An Evidence-Based Framework For Heterogeneous Electronic Health Records: A Case Study In Mortality Prediction.
Proceedings of the Belief Functions: Theory and Applications, 2024

An Evidential Time-to-Event Prediction Model Based on Gaussian Random Fuzzy Numbers.
Proceedings of the Belief Functions: Theory and Applications, 2024

2023
Medical image segmentation with belief function theory and deep learning. (Segmentation d'images médicales avec la théorie de la fonction de croyance et l'apprentissage en profondeur).
PhD thesis, 2023

Application of belief functions to medical image segmentation: A review.
Inf. Fusion, 2023

Semi-supervised multiple evidence fusion for brain tumor segmentation.
Neurocomputing, 2023

Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation.
CoRR, 2023

2022
Lymphoma segmentation from 3D PET-CT images using a deep evidential network.
Int. J. Approx. Reason., 2022

Application of belief functions to medical image segmentation: A review.
CoRR, 2022

Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

2021
Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation.
Proceedings of the Machine Learning in Medical Imaging - 12th International Workshop, 2021

Belief Function-Based Semi-Supervised Learning For Brain Tumor Segmentation.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

Covid-19 Classification with Deep Neural Network and Belief Functions.
Proceedings of the BIBE 2021: The Fifth International Conference on Biological Information and Biomedical Engineering, 2021

Evidential Segmentation of 3D PET/CT Images.
Proceedings of the Belief Functions: Theory and Applications, 2021


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