Purpose Providing the confidence level (Uncertainty) of prediction results and guiding
patient-specific quality assurance (pQA) can enhance the safety of AI (Artificial intelligence) …

We propose a novel method to capture data points near decision boundary in neural
network that are often referred to a specific type of uncertainty. In our approach, we sought to …

We present an automatic large language model (LLM) conversion approach that produces
uncertainty-aware LLMs capable of estimating uncertainty with every prediction. Our …

The development of Brain–Computer Interfaces based on Motor Imagery (MI) tasks is a
relevant research topic worldwide. The design of accurate and reliable BCI systems remains …

Bayesian neural networks (BNNs) are widely used in medical image segmentation tasks
because they provide a probabilistic view of deep learning models by placing a prior …

Uncertainty quantification is a critical aspect of machine learning models, providing
important insights into the reliability of predictions and aiding the decision-making process in …

In recent years, data-driven modeling approaches have gained considerable traction in
various meteorological applications, particularly in the realm of weather forecasting …

Background The deterministic deep learning models have achieved state-of-the-art
performance in various medical image analysis tasks, including nuclei segmentation from …

Data augmentation is widely used to improve the generalization ability of convolutional
neural networks in the image domain. The conventional augmentation schemes, eg, single …

Uncertainty assessment has gained rapid interest in medical image analysis. A popular
technique to compute epistemic uncertainty is the Monte-Carlo (MC) dropout technique …