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Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.
Computer methods and programs in biomedicine
Jun 10, 2019
BACKGROUND AND OBJECTIVE: The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, sleep stages are usually labelled by sleep experts through visually inspecting the whole-night polyso...
Principal Component Analysis based on Nuclear norm Minimization.
Neural networks : the official journal of the International Neural Network Society
Jun 8, 2019
Principal component analysis (PCA) is a widely used tool for dimensionality reduction and feature extraction in the field of computer vision. Traditional PCA is sensitive to outliers which are common in empirical applications. Therefore, in recent ye...
Development of Adverse Outcome Pathway for PPARĪ³ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach.
Chemical research in toxicology
Jun 7, 2019
Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated ...
An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks.
Computer methods and programs in biomedicine
Jun 6, 2019
BACKGROUND AND OBJECTIVE: Chest X-ray (CXR) is one of the most used imaging techniques for detection and diagnosis of pulmonary diseases. A critical component in any computer-aided system, for either detection or diagnosis in digital CXR, is the auto...
Deep convolutional neural networks for mammography: advances, challenges and applications.
BMC bioinformatics
Jun 6, 2019
BACKGROUND: The limitations of traditional computer-aided detection (CAD) systems for mammography, the extreme importance of early detection of breast cancer and the high impact of the false diagnosis of patients drive researchers to investigate deep...
Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.
PloS one
Jun 6, 2019
We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-impl...
Task-Oriented Feature-Fused Network With Multivariate Dataset for Joint Face Analysis.
IEEE transactions on cybernetics
Jun 5, 2019
Deep multitask learning for face analysis has received increasing attentions. From literature, most existing methods focus on optimizing a main task by jointly learning several auxiliary tasks. It is challenging to consider the performance of each ta...
Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network.
Sensors (Basel, Switzerland)
Jun 5, 2019
The classification of electrocardiograms (ECG) plays an important role in the clinical diagnosis of heart disease. This paper proposes an effective system development and implementation for ECG classification based on faster regions with a convolutio...
Deep-Learning Language-Modeling Approach for Automated, Personalized, and Iterative Radiology-Pathology Correlation.
Journal of the American College of Radiology : JACR
Jun 4, 2019
PURPOSE: Radiology-pathology correlation has long been foundational to continuing education, peer learning, quality assurance, and multidisciplinary patient care. The objective of this study was to determine whether modern deep-learning language-mode...
A comparative study on feature selection for a risk prediction model for colorectal cancer.
Computer methods and programs in biomedicine
Jun 4, 2019
BACKGROUND AND OBJECTIVE: Risk prediction models aim at identifying people at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the...
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