Lymphoma segmentation from 3D PET-CT images using a deep evidential network
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Publication:2169207
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
Dempster-Shafer theorybelief functionsevidence theoryuncertainty quantificationdeep learningmedical image analysis
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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
