no code implementations • 12 Sep 2023 • Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis.
no code implementations • 1 Aug 2022 • 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.
1 code implementation • 23 Jun 2022 • Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks.
no code implementations • 3 May 2022 • Ling Huang, Su Ruan, Thierry Denoeux
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation.
no code implementations • 16 Feb 2022 • Thierry Denoeux
We introduce a general theory of epistemic random fuzzy sets for reasoning with fuzzy or crisp evidence.
1 code implementation • 31 Jan 2022 • Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux
The architecture is composed of a deep feature-extraction module and an evidential layer.
2 code implementations • 25 Aug 2021 • Emmanuel Ramasso, Thierry Denoeux, Gael Chevallier
The interpretation of unlabeled acoustic emission (AE) data classically relies on general-purpose clustering methods.
no code implementations • 23 Aug 2021 • Zheng Tong, Philippe Xu, Thierry Denoeux
We propose an information-fusion approach based on belief functions to combine convolutional neural networks.
no code implementations • 11 Aug 2021 • Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan
Single-modality volumes are trained separately to get initial segmentation maps and an evidential fusion layer is proposed to fuse the two pieces of evidence using Dempster-Shafer theory (DST).
1 code implementation • 27 Apr 2021 • Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux
In this paper, a segmentation method based on belief functions is proposed to segment lymphomas in 3D PET/CT images.
no code implementations • 29 Jan 2021 • Ling Huang, Su Ruan, Thierry Denoeux
Precise segmentation of a lesion area is important for optimizing its treatment.
no code implementations • 18 Jan 2021 • Ling Huang, Su Ruan, Thierry Denoeux
Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19.
no code implementations • 3 Oct 2020 • Lianmeng Jiao, Thierry Denoeux, Zhun-Ga Liu, Quan Pan
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference.
no code implementations • 27 Sep 2020 • Thierry Denoeux
Evidential clustering is an approach to clustering based on the use of Dempster-Shafer mass functions to represent cluster-membership uncertainty.
no code implementations • 24 Apr 2020 • 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 Dempster-Shafer theory of belief functions and possibility theory.
no code implementations • 13 Dec 2019 • Thierry Denoeux, Prakash P. Shenoy
The main goal of this paper is to describe an axiomatic utility theory for Dempster-Shafer belief function lotteries.
no code implementations • 12 Dec 2019 • Thierry Denoeux
We then construct an evidential partition such that the pairwise belief and plausibility degrees approximate the bounds of the confidence intervals.
no code implementations • 16 Aug 2018 • Thierry Denoeux
Approaches to decision-making under uncertainty in the belief function framework are reviewed.
no code implementations • 5 Jul 2018 • 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 or higher-level features into Dempster-Shafer mass functions and aggregating them by Dempster's rule of combination.