Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions
and relies on accurate segmentation of structures of interest. Deep learning-based …

Medical image segmentation is an important tool for current clinical applications. It is the
backbone of numerous clinical diagnosis methods, oncological treatments and computer …

Many neurological diseases and delineating pathological regions have been analyzed, and
the anatomical structure of the brain researched with the aid of magnetic resonance imaging …

This study investigates a 3D and fully convolutional neural network (CNN) for subcortical
brain structure segmentation in MRI. 3D CNN architectures have been generally avoided …

Deep learning algorithms have recently provided new ideas for various change detection
(CD) tasks, which have yielded promising results. However, accurately identifying urban …

In this paper, we propose an end-to-end trainable Convolutional Neural Network (CNN)
architecture called the M-net, for segmenting deep (human) brain structures from Magnetic …

Correctly classifying a skin lesion is one of the first steps towards treatment. We propose a
novel convolutional neural network (CNN) architecture for skin lesion classification designed …

Accurate lesion segmentation based on endoscopy images is a fundamental task for the
automated diagnosis of gastrointestinal tract (GI Tract) diseases. Previous studies usually …

Mineral and hydrocarbon exploration relies heavily on geological and geotechnical
information extracted from drill cores. Traditional drill-core characterization is based purely …