212 citations · 850 across the 18 of their papers we have counts for
19 papers
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers
Thierry Denoeux
We introduce a distance-based neural network model for regression, in which prediction uncertainty is quantified by a belief function on the real line. The model interprets the dis…
A Distributional Approach for Soft Clustering Comparison and Evaluation
Andrea Campagner, Davide Ciucci, Thierry Denœux
The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison mea…
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…
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models
Thierry Denoeux
We introduce a general theory of epistemic random fuzzy sets for reasoning with fuzzy or crisp evidence. This framework generalizes both the Dempster-Shafer theory of belief functi…
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 (…
Fusion of evidential CNN classifiers for image classification
Zheng Tong, Philippe Xu, Thierry Denoeux
We propose an information-fusion approach based on belief functions to combine convolutional neural networks. In this approach, several pre-trained DS-based CNN architectures extra…
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…
Clustering acoustic emission data streams with sequentially appearing clusters using mixture models
Emmanuel Ramasso, Thierry Denoeux, Gael Chevallier
The interpretation of unlabeled acoustic emission (AE) data classically relies on general-purpose clustering methods. While several external criteria have been used in the past to…
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…
An evidential classifier based on Dempster-Shafer theory and deep learning
Zheng Tong, Philippe Xu, Thierry Denœux
We propose a new classifier based on Dempster-Shafer (DS) theory and a convolutional neural network (CNN) architecture for set-valued classification. In this classifier, called the…
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…
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering
Lianmeng Jiao, Thierry Denoeux, Zhun-ga Liu +1
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new mode…
NN-EVCLUS: Neural Network-based Evidential Clustering
Thierry Denoeux
Evidential clustering is an approach to clustering based on the use of Dempster-Shafer mass functions to represent cluster-membership uncertainty. In this paper, we introduce a neu…
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence
Thierry Denoeux
We revisit Zadeh's notion of "evidence of the second kind" and show that it provides the foundation for a general theory of epistemic random fuzzy sets, which generalizes both the…
An Interval-Valued Utility Theory for Decision Making with Dempster-Shafer Belief Functions
Thierry Denoeux, Prakash P. Shenoy
The main goal of this paper is to describe an axiomatic utility theory for Dempster-Shafer belief function lotteries. The axiomatic framework used is analogous to von Neumann-Morge…
Calibrated model-based evidential clustering using bootstrapping
Thierry Denoeux
Evidential clustering is an approach to clustering in which cluster-membership uncertainty is represented by a collection of Dempster-Shafer mass functions forming an evidential pa…
Decision-Making with Belief Functions: a Review
Thierry Denoeux
Approaches to decision-making under uncertainty in the belief function framework are reviewed. Most methods are shown to blend criteria for decision under ignorance with the maximu…
Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective
Thierry Denoeux
We revisit logistic regression and its nonlinear extensions, including multilayer feedforward neural networks, by showing that these classifiers can be viewed as converting input o…