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

Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …

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

In semi-supervised medical image segmentation, most previous works draw on the common
assumption that higher entropy means higher uncertainty. In this paper, we investigate a …

The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …

Supervised deep learning models have proven to be highly effective in classification of
dermatological conditions. These models rely on the availability of abundant labeled training …

Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed
early. Many skin lesions are similar in the early stages, which causes an inaccurate …

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 …

Background: Deep neural networks are showing impressive results in different medical
image classification tasks. However, for real-world applications, there is a need to estimate …

Human-Object Interaction (HOI) detection recognizes how persons interact with objects,
which is advantageous in autonomous systems such as self-driving vehicles and …