The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …

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

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

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 …

Background We propose a decision-referral approach for integrating artificial intelligence
(AI) into the breast-cancer screening pathway, whereby the algorithm makes predictions on …

As artificial intelligence (AI) systems begin to make their way into clinical radiology practice,
it is crucial to assure that they function correctly and that they gain the trust of experts …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

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

Machine learning–based medical anomaly detection is an important problem that has been
extensively studied. Numerous approaches have been proposed across various medical …