Recently, multi-modal vision-language foundation models have gained
sign...
Deep neural networks that approximate nonlinear function-to-function
map...
In the realm of urban transportation, metro systems serve as crucial and...
The ability to incrementally learn new classes from limited samples is
c...
Traffic systems can operate in different modes. In a previous work, we
i...
This paper evaluates the downlink performance of cellular networks in te...
One of the innovations brought by Mirai and its derived malware is the
a...
Recent advances in 3D point cloud analysis bring a diverse set of networ...
Magnetic Resonance Imaging (MRI) has become an important technique in th...
Volumetric magnetic resonance (MR) image segmentation plays an important...
Multi-parametric magnetic resonance (MR) imaging is an indispensable too...
Brain tumor segmentation based on multi-modal magnetic resonance imaging...
Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliar...
In Self-Supervised Learning (SSL), various pretext tasks are designed fo...
Cognitive diagnosis is a fundamental yet critical research task in the f...
Big data has grasped great attention in different fields over recent yea...
Lately, deep learning has been extensively investigated for accelerating...
Learning from audio-visual data offers many possibilities to express
cor...
Self-contained loaders are widely adopted in botnets for injecting loadi...
Pre-trained language models (PLM) have demonstrated their effectiveness ...
Parallel imaging is widely used in magnetic resonance imaging as an
acce...
Decreasing magnetic resonance (MR) image acquisition times can potential...
Recovering high-quality images from undersampled measurements is critica...
Triplet loss, one of the deep metric learning (DML) methods, is to learn...
In a motorway network, correlations between the different links, i.e. be...
With the successful application of deep learning in magnetic resonance
i...
The integration of compressed sensing and parallel imaging (CS-PI) provi...
A large number of coils are able to provide enhanced signal-to-noise rat...
Radiomics and deep learning have shown high popularity in automatic glio...
Enumerating all connected induced subgraphs of a given order k is a
comp...
Enumerating all connected subgraphs of a given order from graphs is a
co...
Radiation therapy (RT) is widely employed in the clinic for the treatmen...
Recent studies have witnessed the effectiveness of 3D convolutions on
se...
Image reconstruction from undersampled k-space data plays an important r...
Purpose: Although recent deep energy-based generative models (EBMs) have...
Electronic health record (EHR) coding is the task of assigning ICD codes...
Time-frequency masking or spectrum prediction computed via short symmetr...
This paper presents the details of the Audio-Visual Scene Classification...
Forecasting the (open-high-low-close)OHLC data contained in candlestick ...
The (open-high-low-close) OHLC data is the most common data form in the ...
Accurate image segmentation is crucial for medical imaging applications....
Noises, artifacts, and loss of information caused by the magnetic resona...
Deep learning, particularly the generative model, has demonstrated treme...
Multi-contrast magnetic resonance (MR) image registration is essential i...
Existing adversarial domain adaptation methods mainly consider the margi...
Positron emission tomography (PET) is widely used in clinical practice.
...
To improve the compressive sensing MRI (CS-MRI) approaches in terms of f...
This paper proposes to learn analysis transform network for dynamic magn...
Assessing the location and extent of lesions caused by chronic stroke is...
Parallel imaging has been an essential technique to accelerate MR imagin...