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 …

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 …

Deep learning-based edge detectors heavily rely on pixel-wise labels which are often
provided by multiple annotators. Existing methods fuse multiple annotations using a simple …

Semantic segmentation has made significant progress in recent years thanks to deep neural
networks, but the common objective of generating a single segmentation output that …

Medical image segmentation is a challenging task with inherent ambiguity and high
uncertainty attributed to factors such as unclear tumor boundaries and multiple plausible …

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 …

Despite technological and medical advances, the detection, interpretation, and treatment of
cancer based on imaging data continue to pose significant challenges. These include inter …

Salient object detection (SOD) is a challenging task in computer vision. Current SOD
approaches have made significant progress, but they fail in challenging scenarios. This …

Generating diverse plausible outputs from a single input is crucial for addressing visual
ambiguities, exemplified in medical imaging where experts may provide varying semantic …