AI Medical Compendium Topic:
Signal-To-Noise Ratio

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Heart sound classification based on improved MFCC features and convolutional recurrent neural networks.

Neural networks : the official journal of the International Neural Network Society
Heart sound classification plays a vital role in the early detection of cardiovascular disorders, especially for small primary health care clinics. Despite that much progress has been made for heart sound classification in recent years, most of them ...

High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging.

Biomedical physics & engineering express
The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a necessity during the neurosurgery. However, due to the limitations of conventional imaging systems, hig...

Auditory attention tracking states in a cocktail party environment can be decoded by deep convolutional neural networks.

Journal of neural engineering
OBJECTIVE: A deep convolutional neural network (CNN) is a method for deep learning (DL). It has a powerful ability to automatically extract features and is widely used in classification tasks with scalp electroencephalogram (EEG) signals. However, th...

Subsampled brain MRI reconstruction by generative adversarial neural networks.

Medical image analysis
A main challenge in magnetic resonance imaging (MRI) is speeding up scan time. Beyond improving patient experience and reducing operational costs, faster scans are essential for time-sensitive imaging, such as fetal, cardiac, or functional MRI, where...

Deep learning-based reconstruction of ultrasound images from raw channel data.

International journal of computer assisted radiology and surgery
PURPOSE: We investigate the feasibility of reconstructing ultrasound images directly from raw channel data using a deep learning network. Starting from the raw data, we present the network the full measurement information, allowing for a more generic...

Correction of out-of-FOV motion artifacts using convolutional neural network.

Magnetic resonance imaging
PURPOSE: Subject motion during MRI scan can result in severe degradation of image quality. Existing motion correction algorithms rely on the assumption that no information is missing during motions. However, this assumption does not hold when out-of-...

Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB).

Magnetic resonance imaging
OBJECTIVE: Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI.

Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches.

International journal of computer assisted radiology and surgery
PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. ...

Dual-sensor fusion based attitude holding of a fin-actuated robotic fish.

Bioinspiration & biomimetics
In nature, the lateral line system (LLS) is a critical sensor organ of fish for rheotaxis in complex environments. Inspired by the LLS, numbers of artificial lateral line systems (ALLSs) have been designed to the fish-like robots for flow field perce...

Denoising of multi b-value diffusion-weighted MR images using deep image prior.

Physics in medicine and biology
The clinical value of multiple b-value diffusion-weighted (DW) magnetic resonance imaging (MRI) has been shown in many studies. However, DW-MRI often suffers from low signal-to-noise ratio, especially at high b-values. To address this limitation, we ...