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ORCIDSu Ruan
Publications
- 2025
[j67]Ling Huang
, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation. Inf. Fusion 113: 102648 (2025)- 2024
- [j64]Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng:
A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods. Medical Image Anal. 97: 103223 (2024) - 2023
- [j61]Ling Huang, Su Ruan, Thierry Denoeux:
Semi-supervised multiple evidence fusion for brain tumor segmentation. Neurocomputing 535: 40-52 (2023) - [j60]Ling Huang, Su Ruan, Thierry Denoeux:
Application of belief functions to medical image segmentation: A review. Inf. Fusion 91: 737-756 (2023) - [i28]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation. CoRR abs/2309.05919 (2023) - [i27]Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng:
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods. CoRR abs/2310.06873 (2023) - 2022
- [j54]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. Int. J. Approx. Reason. 149: 39-60 (2022) - [c84]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation. MICCAI (5) 2022: 401-411 - [i25]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. CoRR abs/2201.13078 (2022) - [i22]Ling Huang, Su Ruan:
Application of belief functions to medical image segmentation: A review. CoRR abs/2205.01733 (2022) - [i20]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence fusion with contextual discounting for multi-modality medical image segmentation. CoRR abs/2206.11739 (2022) - 2021
- [c83]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential Segmentation of 3D PET/CT Images. BELIEF 2021: 159-167 - [c82]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 Classification with Deep Neural Network and Belief Functions. BIBE 2021: 3:1-3:4 - [c81]Ling Huang, Su Ruan, Thierry Denoeux:
Belief Function-Based Semi-Supervised Learning For Brain Tumor Segmentation. ISBI 2021: 160-164 - [c79]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation. MLMI@MICCAI 2021: 30-39 - [i19]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 classification with deep neural network and belief functions. CoRR abs/2101.06958 (2021) - [i18]Ling Huang, Su Ruan, Thierry Denoeux:
Belief function-based semi-supervised learning for brain tumor segmentation. CoRR abs/2102.00097 (2021) - [i14]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential segmentation of 3D PET/CT images. CoRR abs/2104.13293 (2021) - [i13]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation. CoRR abs/2108.05422 (2021)
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