Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research
field for many years. In the last decade, intensive developments in deep learning (DL) …

Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US)
fetal images. A number of survey papers in the field is today available, but most of them are …

In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

Tissue analysis is vital for investigating disease mechanisms and guiding diagnostics, eg, in
cancer, communicable or noncommunicable diseases. During the last decades …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

Abdominal anatomy segmentation is crucial for numerous applications from computer-
assisted diagnosis to image-guided surgery. In this context, we address fully-automated …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

The use of deep learning in medical imaging has increased rapidly over the past few years,
finding applications throughout the entire radiology pipeline, from improved scanner …