AIMC Topic: Signal-To-Noise Ratio

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Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Premature ventricular contraction is associated to the risk of coronary heart disease, and its diagnosis depends on a long time heart monitoring. For this purpose, monitoring through Holter devices is often used and computat...

Cycle-consistent adversarial denoising network for multiphase coronary CT angiography.

Medical physics
PURPOSE: In multiphase coronary CT angiography (CTA), a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is signif...

A Machine Hearing System for Robust Cough Detection Based on a High-Level Representation of Band-Specific Audio Features.

IEEE transactions on bio-medical engineering
UNLABELLED: Cough is a protective reflex conveying information on the state of the respiratory system. Cough assessment has been limited so far to subjective measurement tools or uncomfortable (i.e., non-wearable) cough monitors. This limits the pote...

Ultra-Low-Dose Neck CT With Low-Dose Contrast Material for Preoperative Staging of Thyroid Cancer: Image Quality and Diagnostic Performance.

AJR. American journal of roentgenology
OBJECTIVE: Although CT has been used as a complementary diagnostic method for the preoperative diagnosis of thyroid cancer, it has the shortcomings of substantial radiation exposure and the use of contrast material (CM). The purpose of this article i...

Deep learning in medical imaging and radiation therapy.

Medical physics
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these ...

Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection.

Physiological measurement
OBJECTIVE: Atrial fibrillation is a common type of heart rhythm abnormality caused by a problem with the heart's electrical system. Early detection of this disease has important implications for stroke prevention and management. Our objective is to c...

Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Swallowing is a complex process that involves sequential voluntary and involuntary muscle contractions. Malfunctioning of swallowing related muscles could lead to dysphagia. However, there is a lack of standardized and non-invasive methods that suppo...

Developing Noise-Resistant Three-Dimensional Single Particle Tracking Using Deep Neural Networks.

Analytical chemistry
Three-dimensional single particle tracking (3D SPT) is a powerful tool in various chemical and biological studies. In 3D SPT, z sensitive point spread functions (PSFs) are frequently used to generate different patterns, from which the axial position ...

3-D Neural denoising for low-dose Coronary CT Angiography (CCTA).

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
CCTA has become an important tool for coronary arteries assessment in low and medium risk patients. However, it exposes the patient to significant radiation doses, resulting from high image quality requirements and acquisitions at multiple cardiac ph...

Computed tomography super-resolution using deep convolutional neural network.

Physics in medicine and biology
The objective of this study is to develop a convolutional neural network (CNN) for computed tomography (CT) image super-resolution. The network learns an end-to-end mapping between low (thick-slice thickness) and high (thin-slice thickness) resolutio...