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Found 19 papers, 4 papers with code
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis.
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers
We introduce a distance-based neural network model for regression, in which prediction uncertainty is quantified by a belief function on the real line.
Evidence fusion with contextual discounting for multi-modality medical image segmentation
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks.
Application of belief functions to medical image segmentation: A review
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation.
Reasoning with fuzzy and uncertain evidence using epistemic random fuzzy sets: general framework and practical models
We introduce a general theory of epistemic random fuzzy sets for reasoning with fuzzy or crisp evidence.
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
The architecture is composed of a deep feature-extraction module and an evidential layer.
Clustering acoustic emission data streams with sequentially appearing clusters using mixture models
The interpretation of unlabeled acoustic emission (AE) data classically relies on general-purpose clustering methods.
Fusion of evidential CNN classifiers for image classification
We propose an information-fusion approach based on belief functions to combine convolutional neural networks.
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation
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).
Evidential segmentation of 3D PET/CT images
In this paper, a segmentation method based on belief functions is proposed to segment lymphomas in 3D PET/CT images.
Belief function-based semi-supervised learning for brain tumor segmentation
Precise segmentation of a lesion area is important for optimizing its treatment.
Covid-19 classification with deep neural network and belief functions
Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19.
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering
The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference.
NN-EVCLUS: Neural Network-based Evidential Clustering
Evidential clustering is an approach to clustering based on the use of Dempster-Shafer mass functions to represent cluster-membership uncertainty.
Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence
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.
An Interval-Valued Utility Theory for Decision Making with Dempster-Shafer Belief Functions
The main goal of this paper is to describe an axiomatic utility theory for Dempster-Shafer belief function lotteries.
Calibrated model-based evidential clustering using bootstrapping
We then construct an evidential partition such that the pairwise belief and plausibility degrees approximate the bounds of the confidence intervals.
Decision-Making with Belief Functions: a Review
Approaches to decision-making under uncertainty in the belief function framework are reviewed.
Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective
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.
