Confidence calibration is central to providing accurate and interpretable uncertainty
estimates, especially under safety-critical scenarios. However, we find that existing …

In human-AI collaboration systems for critical applications, in order to ensure minimal error,
users should set an operating point based on model confidence to determine when the …

Deep learning (DL) has emerged as a promising tool in healthcare applications. However,
the reproducibility of many studies in this field is limited by the lack of accessible code …

Machine learning (ML) is a commonly employed computer-assisted tool for ECG diagnosis
with above 85% correct. However, the interpretability of the prediction has become a barrier …

For classification models based on neural networks, the maximum predicted class
probability is often used as a confidence score. This score rarely predicts well the probability …

The integration of sophisticated deep learning models into critical domains, such as
healthcare, autonomous vehicles, and the legal system, is increasingly becoming a trend …

Most classifiers based on deep neural networks associate their class prediction with a
probability known as the confidence score. This score is often a by-product of the learning …