Pixel Wise Uncertainty

Pixel-wise uncertainty quantification in image analysis aims to estimate the reliability of each individual pixel prediction made by a model, providing a measure of confidence alongside the prediction itself. Current research focuses on integrating uncertainty estimation into various deep learning architectures, including Bayesian neural networks, teacher-student models, and conformal prediction, across diverse applications like semantic segmentation, medical image analysis, and remote sensing. This capability is crucial for improving the trustworthiness and robustness of AI systems, enabling more reliable decision-making in high-stakes applications where understanding prediction uncertainty is paramount.