Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …

Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and
immediate treatment to improve patient outcome. Machine learning algorithms can be used …

The purpose of this article is to review conventional and advanced neuroimaging techniques
performed in the setting of traumatic brain injury (TBI). The primary goal for the treatment of …

Successive layers in convolutional neural networks (CNN) extract different features from
input images. Applications of CNNs to detect abnormalities in the 2D images or 3D volumes …

Intracranial hemorrhage (ICH) can lead to death or disability, which requires immediate
action from radiologists. Due to the heavy workload, less experienced staff, and the …

Objectives Non-contrast computed tomography of the brain (NCCTB) is commonly used to
detect intracranial pathology but is subject to interpretation errors. Machine learning can …

Automated segmentation of medical imaging is of broad interest to clinicians and machine
learning researchers alike. The goal of segmentation is to increase efficiency and simplicity …

Background: The purpose of this study was to conduct a systematic review for understanding
the availability and limitations of artificial intelligence (AI) approaches that could …

Hemorrhage volume is an important variable in emergently assessing traumatic brain injury
(TBI). The most widely used method for rapid volume estimation is ABC/2, a simple algorithm …

Rapid and accurate diagnosis of intracranial hemorrhage is clinically significant to ensure
timely treatment. In this study, we developed an ensembled deep neural network for the …