Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study
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 …
of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these …
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 …
The multimodal brain tumor image segmentation benchmark (BRATS)
In this paper we report the set-up and results of the Multimodal Brain Tumor Image
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …
Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation
We propose a novel segmentation approach based on deep 3D convolutional encoder
networks with shortcut connections and apply it to the segmentation of multiple sclerosis …
networks with shortcut connections and apply it to the segmentation of multiple sclerosis …
[图书][B] An introduction to outlier analysis
CC Aggarwal, CC Aggarwal - 2017 - Springer
Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …
mining and statistics literature. In most applications, the data is created by one or more …
An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis
In Multiple Sclerosis (MS), detection of T2-hyperintense white matter (WM) lesions on
magnetic resonance imaging (MRI) has become a crucial criterion for diagnosis and …
magnetic resonance imaging (MRI) has become a crucial criterion for diagnosis and …
Graph analysis of functional brain networks: practical issues in translational neuroscience
F de Vico Fallani, J Richiardi… - … Transactions of the …, 2014 - royalsocietypublishing.org
The brain can be regarded as a network: a connected system where nodes, or units,
represent different specialized regions and links, or connections, represent communication …
represent different specialized regions and links, or connections, represent communication …
[HTML][HTML] Test-time adaptable neural networks for robust medical image segmentation
Abstract Convolutional Neural Networks (CNNs) work very well for supervised learning
problems when the training dataset is representative of the variations expected to be …
problems when the training dataset is representative of the variations expected to be …
Q-space deep learning: twelve-fold shorter and model-free diffusion MRI scans
Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines.
An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive …
An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive …
Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images
White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly
individuals and have been associated with various neurological and geriatric disorders. In …
individuals and have been associated with various neurological and geriatric disorders. In …