70 citations · 121 across the 7 of their papers we have counts for
8 papers
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Ling Huang, Su Ruan, Pierre Decazes +1
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis. For this reason, physicians generally diagnose diseases base…
Evidence fusion with contextual discounting for multi-modality medical image segmentation
Ling Huang, Thierry Denoeux, Pierre Vera +1
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks. In this paper, we propose a new deep…
Application of belief functions to medical image segmentation: A review
Ling Huang, Su Ruan, Thierry Denoeux
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation. Belief function theory, a formal framework for uncertaint…
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
Ling Huang, Su Ruan, Pierre Decazes +1
An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (…
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
Ling Huang, Thierry Denoeux, David Tonnelet +2
Lymphoma detection and segmentation from whole-body Positron Emission Tomography/Computed Tomography (PET/CT) volumes are crucial for surgical indication and radiotherapy. Designin…
Evidential segmentation of 3D PET/CT images
Ling Huang, Su Ruan, Pierre Decazes +1
PET and CT are two modalities widely used in medical image analysis. Accurately detecting and segmenting lymphomas from these two imaging modalities are critical tasks for cancer s…
Belief function-based semi-supervised learning for brain tumor segmentation
Ling Huang, Su Ruan, Thierry Denoeux
Precise segmentation of a lesion area is important for optimizing its treatment. Deep learning makes it possible to detect and segment a lesion field using annotated data. However,…
Covid-19 classification with deep neural network and belief functions
Ling Huang, Su Ruan, Thierry Denoeux
Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19. However, visual analysis of CT scans is time-consuming. Thus, it is necessary to d…