Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …

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

Collective insights from a group of experts have always proven to outperform an individual's
best diagnostic for clinical tasks. For the task of medical image segmentation, existing …

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

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

A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE)
is that the ground truth high-quality image is unavailable. Most of the existing methods first …

In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D
saliency detection by learning from the data labeling process. Existing RGB-D saliency …

Despite recent progress of automatic medical image segmentation techniques, fully
automatic results usually fail to meet clinically acceptable accuracy, thus typically require …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …