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 …
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 …
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 …
Multiple sclerosis lesion analysis in brain magnetic resonance images: techniques and clinical applications
Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central
nervous system, characterized by the appearance of focal lesions in the white and gray …
nervous system, characterized by the appearance of focal lesions in the white and gray …
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 …
QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy
Whole brain segmentation from structural magnetic resonance imaging (MRI) is a
prerequisite for most morphological analyses, but is computationally intense and can …
prerequisite for most morphological analyses, but is computationally intense and can …
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS)
patients. Segmentation of the spinal cord and lesions from MRI data provides measures of …
patients. Segmentation of the spinal cord and lesions from MRI data provides measures of …
Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging
SSM Salehi, D Erdogmus… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Brain extraction or whole brain segmentation is an important first step in many of the
neuroimage analysis pipelines. The accuracy and the robustness of brain extraction …
neuroimage analysis pipelines. The accuracy and the robustness of brain extraction …
[HTML][HTML] Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
Abstract Machine learning-based imaging diagnostics has recently reached or even
surpassed the level of clinical experts in several clinical domains. However, classification …
surpassed the level of clinical experts in several clinical domains. However, classification …