Evidential Segmentation of 3D PET/CT Images. - Ling Huang 003; Su Ruan; Pierre Decazes; Thierry Denoeux (BELIEF, 2021)

Covid-19 Classification with Deep Neural Network and Belief Functions. - Ling Huang 003; Su Ruan; Thierry Denoeux (BIBE, 2021)

Belief Function-Based Semi-Supervised Learning For Brain Tumor Segmentation. - Ling Huang 003; Su Ruan; Thierry Denoeux (ISBI, 2021)

Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation. - Ling Huang 003; Thierry Denoeux; David Tonnelet; Pierre Decazes; Su Ruan (MLMI@MICCAI, 2021)

Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation. - Ling Huang 003; Thierry Denoeux; Pierre Vera; Su Ruan (MICCAI (5), 2022)

Application of belief functions to medical image segmentation: A review. - Ling Huang 003; Su Ruan (CoRR, 2022)

Lymphoma segmentation from 3D PET-CT images using a deep evidential network. - Ling Huang 003; Su Ruan; Pierre Decazes; Thierry Denoeux (Int. J. Approx. Reason., 2022)

Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation. - Ling Huang 003; Su Ruan; Pierre Decazes; Thierry Denoeux (CoRR, 2023)

Semi-supervised multiple evidence fusion for brain tumor segmentation. - Ling Huang 003; Su Ruan; Thierry Denoeux (Neurocomputing, 2023)

Application of belief functions to medical image segmentation: A review. - Ling Huang 003; Su Ruan; Thierry Denoeux (Inf. Fusion, 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 003 (2023)

An Evidential Time-to-Event Prediction Model Based on Gaussian Random Fuzzy Numbers. - Ling Huang 003; Yucheng Xing; Thierry Denoeux; Mengling Feng (BELIEF, 2024)

An Evidence-Based Framework For Heterogeneous Electronic Health Records: A Case Study In Mortality Prediction. - Yucheng Ruan; Ling Huang 003; Qianyi Xu; Mengling Feng (BELIEF, 2024)

Evidential time-to-event prediction model with well-calibrated uncertainty estimation. - Ling Huang 003; Yucheng Xing; Swapnil Mishra; Thierry Denoeux; Mengling Feng (CoRR, 2024)

EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers. - Ling Huang 003; Yucheng Xing; Qika Lin; Su Ruan; Mengling Feng (CoRR, 2024)

A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods. - Ling Huang 003; Su Ruan; Yucheng Xing; Mengling Feng (Medical Image Anal., 2024)

Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models. - Qika Lin; Tianzhe Zhao; Kai He 001; Zhen Peng 005; Fangzhi Xu; Ling Huang 003; Jingying Ma; Mengling Feng (ACL (1), 2025)

Evidential time-to-event prediction with calibrated uncertainty quantification. - Ling Huang 003; Yucheng Xing; Swapnil Mishra; Thierry Denoeux; Mengling Feng (Int. J. Approx. Reason., 2025)

Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation. - Ling Huang 003; Su Ruan; Pierre Decazes; Thierry Denoeux (Inf. Fusion, 2025)

Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey. - Qika Lin; Yifan Zhu 001; Xin Mei; Ling Huang 003; Jingying Ma; Kai He 001; Zhen Peng 005; Erik Cambria; Mengling Feng (Inf. Fusion, 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 003; Mengling Feng (CoRR, 2025)

Domain-continual learning for multi-center anatomical detection via prompt-enhanced and densely-fused MedSAM. - Zhan Gao; Ling Huang 003; Qika Lin; Bin Pu; Kai He 001; Mengling Feng; Kenli Li 001 (Inf. Fusion, 2026)