Ongoing improvements in AI, particularly concerning deep learning techniques, are
assisting to identify, classify, and quantify patterns in clinical images. Deep learning is the …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

Abstract Machine learning, particularly deep learning has boosted medical image analysis
over the past years. Training a good model based on deep learning requires large amount …

The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …

Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …

Background Although several deep learning (DL) calcium scoring methods have achieved
excellent performance for specific CT protocols, their performance in a range of CT …

Accurate and automatic organ segmentation is critical for computer-aided analysis towards
clinical decision support and treatment planning. State-of-the-art approaches have achieved …

Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …

In patients with coronary artery stenoses of intermediate severity, the functional significance
needs to be determined. Fractional flow reserve (FFR) measurement, performed during …