As an emerging biomedical image processing technology, medical image segmentation has
made great contributions to sustainable medical care. Now it has become an important …

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 …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

Liver and liver tumor segmentation from 3D volumetric images has been an active research
area in the medical image processing domain for the last few decades. The existence of …

Data diversity is critical to success when training deep learning models. Medical imaging
data sets are often imbalanced as pathologic findings are generally rare, which introduces …

A common shortfall of supervised deep learning for medical imaging is the lack of labeled
data, which is often expensive and time consuming to collect. This article presents a new …

Generative adversarial networks (GANs) and their extensions have carved open many
exciting ways to tackle well known and challenging medical image analysis problems such …