Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

Simple Summary Automatic segmentation of organs and other regions of interest is a
promising approach for reducing the workload of doctors in radiotherapeutic planning, but it …

The Turing test for comparing computer performance to that of humans is well known, but,
surprisingly, there is no widely used test for comparing how much better human-computer …

In light of widely expanding personalized medicine applications and their impact on clinical
outcomes, it is naturally befitting to explore all the dimensional aspects of personalized …

Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-
therapy planning is time-consuming and subject to variation between different observers …

Objective. Delineation of relevant normal tissues is a bottleneck in image-guided precision
radiotherapy workflows for small animals. A deep learning (DL) model for automatic …

Background Heavy smokers are at increased risk for cardiovascular disease and may
benefit from individualized risk quantification using routine lung cancer screening chest …

Mammography is the mainstream imaging modality used for breast cancer screening.
Identification of microcalcifications associated with malignancy may result in early diagnosis …

Objective Pneumothorax is an acute thoracic disease caused by abnormal air collection
between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep …

Machine learning uses historical data to make predictions about new data. It has been
frequently applied in healthcare to optimise diagnostic classification through discovery of …