Deep unsupervised representation learning has recently led to new approaches in the field
of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these …

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 …

The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as
skull-strip**, is an integral component of many neuroimage analysis streams. Despite their …

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …

Data diversity is critical to success when training deep learning models. Medical imaging
data sets are often imbalanced as pathologic findings are generally rare, which introduces …

Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies
conducted with magnetic resonance imaging (MRI) and influences the accuracy of …

Reliably modeling normality and differentiating abnormal appearances from normal cases is
a very appealing approach for detecting pathologies in medical images. A plethora of such …

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural
Network for the challenging task of brain lesion segmentation. The devised architecture is …

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 …

Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging
workflows. Current methods demonstrate good results on non-enhanced T1-weighted …