AIMC Topic: Deep Learning

Clear Filters Showing 18641 to 18650 of 28423 articles

A novel deep-learning-based approach for automatic reorientation of 3D cardiac SPECT images.

European journal of nuclear medicine and molecular imaging
PURPOSE: Reconstructed transaxial cardiac SPECT images need to be reoriented into standard short-axis slices for subsequent accurate processing and analysis. We proposed a novel deep-learning-based method for fully automatic reorientation of cardiac ...

Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques.

Annual review of biomedical engineering
The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use...

Deep Transfer Learning Approach for Automatic Recognition of Drug Toxicity and Inhibition of SARS-CoV-2.

Viruses
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes COVID-19 and is responsible for the ongoing pandemic. Screening of potential antiviral drugs against SARS-CoV-2 depend on in vitro experiments, which are based on the quantification ...

Efficient Computation Reduction in Bayesian Neural Networks Through Feature Decomposition and Memorization.

IEEE transactions on neural networks and learning systems
The Bayesian method is capable of capturing real-world uncertainties/incompleteness and properly addressing the overfitting issue faced by deep neural networks. In recent years, Bayesian neural networks (BNNs) have drawn tremendous attention to artif...

A Survey on Learning-Based Approaches for Modeling and Classification of Human-Machine Dialog Systems.

IEEE transactions on neural networks and learning systems
With the rapid development from traditional machine learning (ML) to deep learning (DL) and reinforcement learning (RL), dialog system equipped with learning mechanism has become the most effective solution to address human-machine interaction proble...

Iterative Dynamic Generic Learning for Face Recognition From a Contaminated Single-Sample Per Person.

IEEE transactions on neural networks and learning systems
This article focuses on a new and practical problem in single-sample per person face recognition (SSPP FR), i.e., SSPP FR with a contaminated biometric enrolment database (SSPP-ce FR), where the SSPP-based enrolment database is contaminated by nuisan...

Deep learning: A primer for psychologists.

Psychological methods
Deep learning has revolutionized predictive modeling in topics such as computer vision and natural language processing but is not commonly applied to psychological data. In an effort to bring the benefits of deep learning to psychologists, we provide...

Deep ANC: A deep learning approach to active noise control.

Neural networks : the official journal of the International Neural Network Society
Traditional active noise control (ANC) methods are based on adaptive signal processing with the least mean square algorithm as the foundation. They are linear systems and do not perform satisfactorily in the presence of nonlinear distortions. In this...

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

Medical image analysis
The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We de...

A deep learning based surrogate model for the parameter identification problem in probabilistic cellular automaton epidemic models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: an accurate estimation of the epidemiological model coefficients helps understand the basic principles of disease spreading. Some studies showed that dozens of hours are needed to simulate the traditional probabilistic cellu...