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Databases, Factual
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A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis.
Computer methods and programs in biomedicine
May 13, 2019
BACKGROUND AND OBJECTIVE: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients.
DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images.
PLoS computational biology
May 13, 2019
Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic ...
A new approach for arrhythmia classification using deep coded features and LSTM networks.
Computer methods and programs in biomedicine
May 10, 2019
BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor hav...
Arrhythmias, Cardiac
Pattern Recognition, Automated
Data Compression
Principal Component Analysis
Signal Processing, Computer-Assisted
Neural Networks, Computer
Electrocardiography, Ambulatory
Reproducibility of Results
Software
Data Management
Humans
Electrocardiography
Algorithms
Databases, Factual
Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting.
IEEE transactions on bio-medical engineering
May 9, 2019
OBJECTIVE: This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imb...
Detection of smoking status from retinal images; a Convolutional Neural Network study.
Scientific reports
May 9, 2019
Cardiovascular diseases are directly linked to smoking habits, which has both physiological and anatomical effects on the systemic and retinal circulations, and these changes can be detected with fundus photographs. Here, we aimed to 1- design a Conv...
Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders.
IEEE transactions on medical imaging
May 6, 2019
Current deep supervised learning methods typically require large amounts of labeled data for training. Since there is a significant cost associated with clinical data acquisition and labeling, medical datasets used for training these models are relat...
A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems.
Neural networks : the official journal of the International Neural Network Society
May 4, 2019
This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use...
Recurrent Neural Networks With External Addressable Long-Term and Working Memory for Learning Long-Term Dependences.
IEEE transactions on neural networks and learning systems
May 3, 2019
Learning long-term dependences (LTDs) with recurrent neural networks (RNNs) is challenging due to their limited internal memories. In this paper, we propose a new external memory architecture for RNNs called an external addressable long-term and work...
Saliency From Growing Neural Gas: Learning Pre-Attentional Structures for a Flexible Attention System.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
May 2, 2019
Artificial visual attention has been an active research area for over two decades. Especially, the concept of saliency has been implemented in many different ways. Early approaches aimed at closely modeling saliency processing with concepts from biol...
Sample Fusion Network: An End-to-End Data Augmentation Network for Skeleton-Based Human Action Recognition.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
May 2, 2019
Data augmentation is a widely used technique for enhancing the generalization ability of deep neural networks for skeleton-based human action recognition (HAR) tasks. Most existing data augmentation methods generate new samples by means of handcrafte...
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