AI RESEARCH
Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification
arXiv CS.CV
•
ArXi:2603.09359v1 Announce Type: new Physics-informed neural networks (PINNs) have shown promise in addressing the ill-posed deconvolution problem in computed tomography perfusion (CTP) imaging for acute ischemic stroke assessment. However, existing PINN-based approaches remain deterministic and do not quantify uncertainty associated with violations of physics constraints, limiting reliability assessment.