Abstract A 3-dimensional (3D) convolutional neural network is presented for the
segmentation and quantification of spontaneous intracerebral haemorrhage (ICH) in non …

Current hardware restrictions pose limitations on the use of convolutional neural networks
for medical image analysis. There is a large trade-off between network architecture and …

We develop a deep learning approach for automated intracerebral hemorrhage (ICH)
segmentation from 3D computed tomography (CT) scans. Our model, ICHNet, evolves by …

Intracerebral hemorrhage (ICH) is the most serious type of stroke, which results in a high
disability or mortality rate. Therefore, accurate and rapid ICH region segmentation is of great …

Authors implemented an artificial intelligence (AI)–based detection tool for intracranial
hemorrhage (ICH) on noncontrast CT images into an emergent workflow, evaluated its …

Objectives Aimed to develop a nomogram model based on deep learning features and
radiomics features for the prediction of early hematoma expansion. Methods A total of 561 …

Intracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. This leads
to significant morbidity and mortality, the likelihood of which is predicated on the size of the …

Traumatic brain injuries may cause intracranial hemorrhages (ICH). ICH could lead to
disability or death if it is not accurately diagnosed and treated in a time-sensitive procedure …

Objectives Artif icial intelligence (AI)–based image analysis is increasingly applied in the
acute stroke field. Its implementation for the detection and quantification of hemorrhage …

This project aimed to develop and evaluate a fast and fully-automated deep-learning
method applying convolutional neural networks with deep supervision (CNN-DS) for …