The increasing adoption of the whole slide image (WSI) technology in histopathology has
dramatically transformed pathologists' workflow and allowed the use of computer systems in …

Calibrated probabilistic classifiers are models whose predicted probabilities can directly be
interpreted as uncertainty estimates. It has been shown recently that deep neural networks …

Breast histology image classification is a crucial step in the early diagnosis of breast cancer.
In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have …

Abstract Deep Neural Networks (DNNs) are known to make overconfident mistakes, which
makes their use problematic in safety-critical applications. State-of-the-art (SOTA) calibration …

In medical image classification tasks, it is common to find that the number of normal samples
far exceeds the number of abnormal samples. In such class-imbalanced situations, reliable …

Albeit revealing impressive predictive performance for several computer vision tasks, deep
neural networks (DNNs) are prone to making overconfident predictions. This limits the …

Modern visual recognition models often display overconfidence due to their reliance on
complex deep neural networks and one-hot target supervision, resulting in unreliable …

Albeit achieving high predictive accuracy across many challenging computer vision
problems, recent studies suggest that deep neural networks (DNNs) tend to make …

With deep neural network based solution more readily being incorporated in real-world
applications, it has been pressing requirement that predictions by such models, especially in …

Deep neural networks (DNNs) have enabled astounding progress in several vision-based
problems. Despite showing high predictive accuracy, recently, several works have revealed …