Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification

Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification explores EPPINN enhances computed tomography perfusion imaging by integrating uncertainty quantification with physics-informed neural networks for improved stroke assessment.. Commercial viability score: 7/10 in Medical AI.