Featured Application This review provides a critical review of deep/machine learning
algorithms used in the identification of ischemic stroke and demyelinating brain diseases. It …

Objective To examine the capability of MRI texture analysis to differentiate the primary site of
origin of brain metastases following a radiomics approach. Methods Sixty-seven untreated …

Recently, learning algorithms such as Convolutional Neural Networks have been
successfully applied in different stages of data processing from the acquisition to the data …

We propose an adaptation of a convolutional neural network (CNN) scheme proposed for
segmenting brain lesions with considerable mass-effect, to segment white matter …

Background The differential quantification of brain atrophy, white matter hyperintensities
(WMH) and stroke lesions is important in studies of stroke and dementia. However, the …

Rationale Cerebral small vessel disease (SVD) is common in ageing and patients with
dementia and stroke. Its manifestations on magnetic resonance imaging (MRI) include white …

White matter hyperintensities (WMHs) are lesion in brain magnetic resonance images
generally associated with Alzheimer's disease (AD) and cognitive decline. Finding WMHs of …

The segmentation of white matter abnormalities is crucial for the early diagnosis of cerebral
diseases, which aids in minimizing the resultant physical and cognitive deficits. Automated …

In the wake of the use of deep learning algorithms in medical image analysis, we compared
performance of deep learning algorithms, namely the deep Boltzmann machine (DBM) …

Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality
in ischemic stroke. We propose a machine learning technique to automatically identify …