AIMC Topic: Spatio-Temporal Analysis

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CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions.

Scientific reports
Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular (LV) coverage in a single breath-hold. However, 3D imaging remains limited t...

Label-Free Quantification of Pharmacokinetics in Skin with Stimulated Raman Scattering Microscopy and Deep Learning.

The Journal of investigative dermatology
The treatment of inflammatory skin conditions relies on a deep understanding of how drugs and tissue behave and interact. Although numerous methods have been developed that aim to follow and quantify topical drug pharmacokinetics, these tools can com...

Neural dynamics of perceptual inference and its reversal during imagery.

eLife
After the presentation of a visual stimulus, neural processing cascades from low-level sensory areas to increasingly abstract representations in higher-level areas. It is often hypothesised that a reversal in neural processing underlies the generatio...

Diagnostic performance of fetal intelligent navigation echocardiography (FINE) in fetuses with double-outlet right ventricle (DORV).

The international journal of cardiovascular imaging
The main objective of this study was to investigate the diagnostic performance of FINE in generating and displaying 3 specific abnormal fetal echocardiography views such as left ventricular outflow tract (LVOT) view, right ventricular outflow tract (...

Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network.

Sensors (Basel, Switzerland)
Emotion recognition plays an important role in the field of human-computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate human emotion owing to its convenience and mobility. Deep neural network (DNN) approaches using an ...

Hierarchical Organization of Functional Brain Networks Revealed by Hybrid Spatiotemporal Deep Learning.

Brain connectivity
Hierarchical organization of brain function has been an established concept in the neuroscience field for a long time, however, it has been rarely demonstrated how such hierarchical macroscale functional networks are actually organized in the human b...

ChronoMID-Cross-modal neural networks for 3-D temporal medical imaging data.

PloS one
ChronoMID-neural networks for temporally-varying, hence Chrono, Medical Imaging Data-makes the novel application of cross-modal convolutional neural networks (X-CNNs) to the medical domain. In this paper, we present multiple approaches for incorporat...

An efficient analytical reduction of detailed nonlinear neuron models.

Nature communications
Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of ...

Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning.

Geospatial health
Early warning systems (EWS) have been proposed as a measure for controlling and preventing dengue fever outbreaks in countries where this infection is endemic. A vaccine is not available and has yet to reach the market due to the economic burden of d...

A dictionary learning approach for spatio-temporal characterization of absence seizures.

Physiological measurement
OBJECTIVE: This research explores absence seizures using data recorded from different layers of somatosensory cortex of four genetic absence epilepsy rats from Strasbourg (GAERS). Localizing the active layers of somatosensory cortex (spatial analysis...