AIMC Topic: Deep Learning

Clear Filters Showing 21831 to 21840 of 28423 articles

An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset.

Journal of healthcare engineering
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a chal...

Applications of Artificial Intelligence in Cardiology. The Future is Already Here.

Revista espanola de cardiologia (English ed.)
There is currently no other hot topic like the ability of current technology to develop capabilities similar to those of human beings, even in medicine. This ability to simulate the processes of human intelligence with computer systems is known as ar...

Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics.

NeuroImage
There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there...

Privacy-enhanced multi-party deep learning.

Neural networks : the official journal of the International Neural Network Society
In multi-party deep learning, multiple participants jointly train a deep learning model through a central server to achieve common objectives without sharing their private data. Recently, a significant amount of progress has been made toward the priv...

Face Hallucination Using Cascaded Super-Resolution and Identity Priors.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper we address the problem of hallucinating high-resolution facial images from low-resolution inputs at high magnification factors. We approach this task with convolutional neural networks (CNNs) and propose a novel (deep) face hallucinatio...

The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns.

Neural networks : the official journal of the International Neural Network Society
The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series a...

Deep representation learning for individualized treatment effect estimation using electronic health records.

Journal of biomedical informatics
Utilizing clinical observational data to estimate individualized treatment effects (ITE) is a challenging task, as confounding inevitably exists in clinical data. Most of the existing models for ITE estimation tackle this problem by creating unbiased...

Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.

Scientific reports
Broadly neutralizing antibodies (bNAbs) targeting the HIV-1 envelope glycoprotein (Env) have promising utility in prevention and treatment of HIV-1 infection, and several are currently undergoing clinical trials. Due to the high sequence diversity an...