Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …

In this review the application of deep learning for medical diagnosis is addressed. A
thorough analysis of various scientific articles in the domain of deep neural networks …

Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …

In recent years, deep convolutional neural networks (CNNs) have shown record-shattering
performance in a variety of computer vision problems, such as visual object recognition …

Detailed whole brain segmentation is an essential quantitative technique in medical image
analysis, which provides a non-invasive way of measuring brain regions from a clinical …

Deep learning is a form of machine learning using a convolutional neural network
architecture that shows tremendous promise for imaging applications. It is increasingly being …

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 …

Brain is an amazing organ that controls all activities of a human. Any abnormality in the
shape of anatomical regions of the brain needs to be detected as early as possible to reduce …

Whole brain segmentation of fine-grained structures using deep learning (DL) is a very
challenging task since the number of anatomical labels is very high compared to the number …

Rationale and Objectives The automated segmentation of organs and tissues throughout the
body using computed tomography and magnetic resonance imaging has been rapidly …