Convolutional neural networks for radiologic images: a radiologist's guide
S Soffer, A Ben-Cohen, O Shimon, MM Amitai… - Radiology, 2019 - pubs.rsna.org
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 …
recently gained particular attention in the radiology community. This article provides an …
Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
Glymphatic system impairment in multiple sclerosis: relation with brain damage and disability
Recent evidence has shown the existence of a CNS 'waste clearance'system, defined as the
glymphatic system. Glymphatic abnormalities have been described in several …
glymphatic system. Glymphatic abnormalities have been described in several …
Boundary loss for highly unbalanced segmentation
H Kervadec, J Bouchtiba, C Desrosiers… - … on medical imaging …, 2019 - proceedings.mlr.press
Widely used loss functions for convolutional neural network (CNN) segmentation, eg, Dice
or cross-entropy, are based on integrals (summations) over the segmentation regions …
or cross-entropy, are based on integrals (summations) over the segmentation regions …
Tversky loss function for image segmentation using 3D fully convolutional deep networks
Fully convolutional deep neural networks carry out excellent potential for fast and accurate
image segmentation. One of the main challenges in training these networks is data …
image segmentation. One of the main challenges in training these networks is data …
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected
units are used to extract patterns from data in order to solve complex problems. Deep …
units are used to extract patterns from data in order to solve complex problems. Deep …
HyperDense-Net: a hyper-densely connected CNN for multi-modal image segmentation
Recently, dense connections have attracted substantial attention in computer vision
because they facilitate gradient flow and implicit deep supervision during training …
because they facilitate gradient flow and implicit deep supervision during training …
Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
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 …
performance in a variety of computer vision problems, such as visual object recognition …
3D conditional generative adversarial networks for high-quality PET image estimation at low dose
Positron emission tomography (PET) is a widely used imaging modality, providing insight
into both the biochemical and physiological processes of human body. Usually, a full dose …
into both the biochemical and physiological processes of human body. Usually, a full dose …
Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin
is of key importance in many neurological research studies. Currently, measurements are …
is of key importance in many neurological research studies. Currently, measurements are …