Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The
potential applications are vast and include the entirety of the medical imaging life cycle from …

The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

Brain tumor is one of the most dangerous cancers in people of all ages, and its grade
recognition is a challenging problem for radiologists in health monitoring and automated …

Recent advances in deep learning for medical image segmentation demonstrate expert-
level accuracy. However, application of these models in clinically realistic environments can …

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …

Artificial intelligence (AI)—the ability of a machine to perform cognitive tasks to achieve a
particular goal based on provided data—is revolutionizing and resha** our health-care …

Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-
the-art results in semantic segmentation for numerous medical imaging applications …

The current diagnostic pathway for prostate cancer has resulted in overdiagnosis and
consequent overtreatment as well as underdiagnosis and missed diagnoses in many men …

In radiomics studies, researchers usually need to develop a supervised machine learning
model to map image features onto the clinical conclusion. A classical machine learning …