Lymphoma segmentation from 3D PET-CT images using a deep evidential network

DOI10.1016/j.ijar.2022.06.007OpenAlexW4226109042MaRDI QIDQ2169207

Pierre Decazes, Ling Huang, Su Ruan, Thierry Denoeux

Publication date: 2 September 2022

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2201.13078

zbMATH Keywords

Dempster-Shafer theorybelief functionsevidence theoryuncertainty quantificationdeep learningmedical image analysis

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items

An evidential neural network model for regression based on random fuzzy numbers ⋮ Special issue from the 6th international conference on belief functions (BELIEF 2021)

Uses Software

• conover.test
• SimpleITK
• U-Net
• V-Net

Cites Work

• Unnamed Item
• Proposition and learning of some belief function contextual correction mechanisms
• The transferable belief model
• The control of the false discovery rate in multiple testing under dependency.
• Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
• Evidential instance selection for \(K\)-nearest neighbor classification of big data
• Cautious classification based on belief functions theory and imprecise relabelling
• Fast semi-supervised evidential clustering
• Belief functions clustering for epipole localization
• A new evidential \(K\)-nearest neighbor rule based on contextual discounting with partially supervised learning
• Decision-making with belief functions: a review
• Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions