PEEL: Physics-Enabled Evidential Learning for Identifiable Uncertainty in CT Imaging

arXiv:2609.29599 · cs.AI, physics.med-ph · Submitted 2026-08-27 · Read on arXiv

cs.AI, physics.med-ph

Submitted: 2026-08-27

Updated: 2026-08-27

Comments: 10 pages, 3 figures, 1 table. Proof-of-concept study of physics-enabled identification of NIG uncertainty in CT imaging

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Monte Carlo — 8× in this paper
• Physics-Enabled Evidential Learning — 4× in this paper
• Calibration — 3× in this paper · explained in 7 episodes
• Admissibility — 2× in this paper · explained in 2 episodes
• Aleatoric Uncertainty — 2× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Uncertainty Decomposition — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• ELVAE: Evidential Learning-Based Variational Autoencoder for Uncertainty-Aware Generation

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