AI Medical Compendium Topic:
Signal-To-Noise Ratio

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Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction.

Computational intelligence and neuroscience
Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive perform...

Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (SECT) images for improved pancreatic cancer imaging quality.

ECG Heartbeat Classification Based on an Improved ResNet-18 Model.

Computational and mathematical methods in medicine
Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique r...

A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI.

Magnetic resonance in medicine
PURPOSE: With rising safety concerns over the use of gadolinium-based contrast agents (GBCAs) in contrast-enhanced MRI, there is a need for dose reduction while maintaining diagnostic capability. This work proposes comprehensive technical solutions f...

Deep Learning-Based Denoising in High-Speed Portable Reflectance Confocal Microscopy.

Lasers in surgery and medicine
BACKGROUND AND OBJECTIVE: Portable confocal microscopy (PCM) is a low-cost reflectance confocal microscopy technique that can visualize cellular details of human skin in vivo. When PCM images are acquired with a short exposure time to reduce motion b...

Incorporation of residual attention modules into two neural networks for low-dose CT denoising.

Medical physics
PURPOSE: The low-dose computed tomography (CT) imaging can reduce the damage caused by x-ray radiation to the human body. However, low-dose CT images have a different degree of artifacts than conventional CT images, and their resolution is lower than...

A noisy label and negative sample robust loss function for DNN-based distant supervised relation extraction.

Neural networks : the official journal of the International Neural Network Society
As a major method for relation extraction, distantly supervised relation extraction (DSRE) suffered from the noisy label problem and class imbalance problem (these two problems are also common for many other NLP tasks, e.g., text classification). How...

Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network.

IEEE transactions on medical imaging
Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastogr...

Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts.

Radiology
Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose T...

Transfer learning in deep neural network-based receiver coil sensitivity map estimation.

Magma (New York, N.Y.)
INTRODUCTION: The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depend...