[HTML][HTML] An overview of deep learning in medical imaging focusing on MRI
AS Lundervold, A Lundervold - Zeitschrift für Medizinische Physik, 2019 - Elsevier
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
Deep learning and the electrocardiogram: review of the current state-of-the-art
In the recent decade, deep learning, a subset of artificial intelligence and machine learning,
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …
has been used to identify patterns in big healthcare datasets for disease phenotyping, event …
[HTML][HTML] Medical deep learning—A systematic meta-review
Deep learning has remarkably impacted several different scientific disciplines over the last
few years. For example, in image processing and analysis, deep learning algorithms were …
few years. For example, in image processing and analysis, deep learning algorithms were …
A U-Net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease
Brain vessel status is a promising biomarker for better prevention and treatment in
cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need …
cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need …
Machine learning in action: stroke diagnosis and outcome prediction
The application of machine learning has rapidly evolved in medicine over the past decade.
In stroke, commercially available machine learning algorithms have already been …
In stroke, commercially available machine learning algorithms have already been …
Applications of deep learning to neuro-imaging techniques
Many clinical applications based on deep learning and pertaining to radiology have been
proposed and studied in radiology for classification, risk assessment, segmentation tasks …
proposed and studied in radiology for classification, risk assessment, segmentation tasks …
A systematic review of machine learning models for predicting outcomes of stroke with structured data
Background and purpose Machine learning (ML) has attracted much attention with the hope
that it could make use of large, routinely collected datasets and deliver accurate …
that it could make use of large, routinely collected datasets and deliver accurate …
Neuroimaging and deep learning for brain stroke detection-A review of recent advancements and future prospects
Background and objective In recent years, deep learning algorithms have created a massive
impact on addressing research challenges in different domains. The medical field also …
impact on addressing research challenges in different domains. The medical field also …
Robustness of radiomic features in magnetic resonance imaging: review and a phantom study
R Cattell, S Chen, C Huang - … computing for industry, biomedicine, and art, 2019 - Springer
Radiomic analysis has exponentially increased the amount of quantitative data that can be
extracted from a single image. These imaging biomarkers can aid in the generation of …
extracted from a single image. These imaging biomarkers can aid in the generation of …
Deep learning in neuroradiology: a systematic review of current algorithms and approaches for the new wave of imaging technology
Purpose To systematically review and synthesize the current literature and to develop a
compendium of technical characteristics of existing deep learning applications in …
compendium of technical characteristics of existing deep learning applications in …