Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

The interest in machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Accurate automated medical image recognition, including classification and segmentation,
is one of the most challenging tasks in medical image analysis. Recently, deep learning …

In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is
proposed for Gastric Histopathological Image Detection (GHID), which enables the …

Neural networks are ubiquitous in many tasks, but trusting their predictions is an open issue.
Uncertainty quantification is required for many applications, and disentangled aleatoric and …

Cervical cancer is the fourth most common cancer among women, and cytopathological
images are often used to screen for this cancer. However, manual examination is very …

The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and
mutual information, respectively, has recently become quite common in machine learning …

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Artificial Intelligence (AI) use in automated Electrocardiogram (ECG) classification has
continuously attracted the research community's interest, motivated by their promising …