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Low-Complexity Approximate Convolutional Neural Networks.
IEEE transactions on neural networks and learning systems
Apr 10, 2018
In this paper, we present an approach for minimizing the computational complexity of the trained convolutional neural networks (ConvNets). The idea is to approximate all elements of a given ConvNet and replace the original convolutional filters and p...
A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography.
Ophthalmology
Apr 10, 2018
PURPOSE: Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these...
Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.
The British journal of radiology
Apr 10, 2018
Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the develo...
Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling.
Radiology
Apr 10, 2018
Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFR) and F...
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Machine learning-aided exploration of relationship between strength and elastic properties in ascending thoracic aneurysm.
International journal for numerical methods in biomedical engineering
Apr 10, 2018
Machine learning was applied to classify tension-strain curves harvested from inflation tests on ascending thoracic aneurysm samples. The curves were classified into rupture and nonrupture groups using prerupture response features. Two groups of feat...
Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning.
Epilepsia
Apr 10, 2018
OBJECTIVE: Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In ...
Stacked generalization: an introduction to super learning.
European journal of epidemiology
Apr 10, 2018
Stacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into a host of methods among which is th...
Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study.
BMJ open
Apr 10, 2018
INTRODUCTION: Mortality and morbidity following surgery are pressing public health concerns in the USA. Traditional prediction models for postoperative adverse outcomes demonstrate good discrimination at the population level, but the ability to forec...
Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers.
Journal of medical systems
Apr 10, 2018
Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is projected that this will reach nearly 10% by 2045. The major challenge is that when machine learning-base...
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Body Weights and Measures
Age Distribution
Sex Distribution
Blood Pressure
Data Interpretation, Statistical
Bayes Theorem
Artificial Intelligence
Reproducibility of Results
Machine Learning
Adult
Decision Support Techniques
Male
Female
Middle Aged
Humans
Blood Glucose
Neural Networks, Computer
United States
Diabetes Mellitus
Indians, North American
An ontological approach to identifying cases of chronic kidney disease from routine primary care data: a cross-sectional study.
BMC nephrology
Apr 10, 2018
BACKGROUND: Accurately identifying cases of chronic kidney disease (CKD) from primary care data facilitates the management of patients, and is vital for surveillance and research purposes. Ontologies provide a systematic and transparent basis for cli...
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