Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …
Semantic segmentation and edge detection—Approach to road detection in very high resolution satellite images
Road detection technology plays an essential role in a variety of applications, such as urban
planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there …
planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there …
Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy …
A Shoeibi, N Ghassemi, M Khodatars, P Moridian… - Cognitive …, 2023 - Springer
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger.
So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and …
So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and …
Attention-enhanced neural network models for turbulence simulation
Deep neural network models have shown great potential in accelerating the simulation of
fluid dynamic systems. Once trained, these models can make inferences within seconds …
fluid dynamic systems. Once trained, these models can make inferences within seconds …
What can computational models learn from human selective attention? A review from an audiovisual unimodal and crossmodal perspective
Selective attention plays an essential role in information acquisition and utilization from the
environment. In the past 50 years, research on selective attention has been a central topic in …
environment. In the past 50 years, research on selective attention has been a central topic in …
Linear attention coupled Fourier neural operator for simulation of three-dimensional turbulence
Modeling three-dimensional (3D) turbulence by neural networks is difficult because 3D
turbulence is highly nonlinear with high degrees of freedom and the corresponding …
turbulence is highly nonlinear with high degrees of freedom and the corresponding …
Spatiotemporal neural network with attention mechanism for El Niño forecasts
To learn spatiotemporal representations and anomaly predictions from geophysical data, we
propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and …
propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and …
Improved deep CNNs based on Nonlinear Hybrid Attention Module for image classification
N Guo, K Gu, J Qiao, J Bi - Neural Networks, 2021 - Elsevier
Recent years have witnessed numerous successful applications of incorporating attention
module into feed-forward convolutional neural networks. Along this line of research, we …
module into feed-forward convolutional neural networks. Along this line of research, we …
Attention-Based Fine-Grained Lightweight Architecture for Fuji Apple Maturity Classification in an Open-World Orchard Environment
L Zhang, Q Hao, J Cao - Agriculture, 2023 - mdpi.com
Fuji apples are one of the most important and popular economic crops worldwide in the fruit
industry. Nowadays, there is a huge imbalance between the urgent demand of precise …
industry. Nowadays, there is a huge imbalance between the urgent demand of precise …
Protein interaction network reconstruction with a structural gated attention deep model by incorporating network structure information
Protein–protein interactions (PPIs) provide a physical basis of molecular communications for
a wide range of biological processes in living cells. Establishing the PPI network has …
a wide range of biological processes in living cells. Establishing the PPI network has …