Ling Huang

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

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models.

Qika Lin, Tianzhe Zhao, Kai He, Zhen Peng, Fangzhi Xu, Ling Huang, Jingying Ma, Mengling Feng

CoRR, January, 2025

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

Yucheng Ruan, Daniel J. Tan, See-Kiong Ng, Ling Huang, Mengling Feng

CoRR, January, 2025

Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey.

Qika Lin, Yifan Zhu, Xin Mei, Ling Huang, Jingying Ma, Kai He, Zhen Peng, Erik Cambria, Mengling Feng

Inf. Fusion, 2025

Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation.

Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux

Inf. Fusion, 2025

2024

A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods.

Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng

Medical Image Anal., 2024

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

Ling Huang, Yucheng Xing, Qika Lin, Su Ruan, Mengling Feng

CoRR, 2024

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

Ling Huang, Yucheng Xing, Swapnil Mishra, Thierry Denoeux, Mengling Feng

CoRR, 2024

An Evidence-Based Framework For Heterogeneous Electronic Health Records: A Case Study In Mortality Prediction.

Yucheng Ruan, Ling Huang, Qianyi Xu, Mengling Feng

Proceedings of the Belief Functions: Theory and Applications, 2024

An Evidential Time-to-Event Prediction Model Based on Gaussian Random Fuzzy Numbers.

Ling Huang, Yucheng Xing, Thierry Denoeux, Mengling Feng

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).

Ling Huang

PhD thesis, 2023

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

Ling Huang, Su Ruan, Thierry Denoeux

Inf. Fusion, 2023

Semi-supervised multiple evidence fusion for brain tumor segmentation.

Ling Huang, Su Ruan, Thierry Denoeux

Neurocomputing, 2023

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

Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux

CoRR, 2023

2022

Lymphoma segmentation from 3D PET-CT images using a deep evidential network.

Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux

Int. J. Approx. Reason., 2022

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

Ling Huang, Su Ruan

CoRR, 2022

Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation.

Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan

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.

Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan

Proceedings of the Machine Learning in Medical Imaging - 12th International Workshop, 2021

Belief Function-Based Semi-Supervised Learning For Brain Tumor Segmentation.

Ling Huang, Su Ruan, Thierry Denoeux

Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

Covid-19 Classification with Deep Neural Network and Belief Functions.

Ling Huang, Su Ruan, Thierry Denoeux

Proceedings of the BIBE 2021: The Fifth International Conference on Biological Information and Biomedical Engineering, 2021

Evidential Segmentation of 3D PET/CT Images.

Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux

Proceedings of the Belief Functions: Theory and Applications, 2021