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 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 …

Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical
images. Existing studies mainly apply transfer learning and other data representation …

Autosegmentation of gross tumor volumes holds promise to decrease clinical demand and
to provide consistency across clinicians and institutions for radiation treatment planning …

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

In this work, we for the first time present a method for detecting labeling errors in image
datasets with semantic segmentation, ie, pixel-wise class labels. Annotation acquisition for …

The recent advances and availability of computer hardware, software tools, and massive
digital data archives have enabled the rapid development of artificial intelligence (AI) …

Background Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC),
where the primary gross tumor volume (GTVp) is manually segmented with high …

The robustness and accuracy of the intensity-based 3D/2D registration of a 3D model on
planar X-ray image (s) is related to the quality of the image correspondences between the …

Objectives: Automated computational segmentation of the lung and its lobes and findings in
X-Ray based computed tomography (CT) images is a challenging problem with important …