AIMC Topic: Neural Networks, Computer

Clear Filters Showing 22031 to 22040 of 31376 articles

An Intelligent Recurrent Neural Network with Long Short-Term Memory (LSTM) BASED Batch Normalization for Medical Image Denoising.

Journal of medical systems
The process of denoising of medical images that are corrupted by noise is considered as a long established setback in the signal or image processing domain. An effective system for denoising in order to remove white, salt and also pepper noises by me...

Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks.

IEEE journal of biomedical and health informatics
In this paper, we propose a bi-modality medical image synthesis approach based on sequential generative adversarial network (GAN) and semi-supervised learning. Our approach consists of two generative modules that synthesize images of the two modaliti...

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE).

IEEE transactions on medical imaging
In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enfor...

Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance ...

Motion estimation and correction in cardiac CT angiography images using convolutional neural networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Cardiac motion artifacts frequently reduce the interpretability of coronary computed tomography angiography (CCTA) images and potentially lead to misinterpretations or preclude the diagnosis of coronary artery disease (CAD). In this paper, a novel mo...

Nervous-Like Circuits in the Ribosome Facts, Hypotheses and Perspectives.

International journal of molecular sciences
In the past few decades, studies on translation have converged towards the metaphor of a "ribosome nanomachine"; they also revealed intriguing ribosome properties challenging this view. Many studies have shown that to perform an accurate protein synt...

Automatic CIN Grades Prediction of Sequential Cervigram Image Using LSTM With Multistate CNN Features.

IEEE journal of biomedical and health informatics
Cervical cancer ranks as the second most common cancer in women worldwide. In clinical practice, colposcopy is an indispensable part of screening for cervical intraepithelial neoplasia (CIN) grades and cervical cancer but exhibits high misdiagnosis r...

Markerless Pancreatic Tumor Target Localization Enabled By Deep Learning.

International journal of radiation oncology, biology, physics
PURPOSE: Deep learning is an emerging technique that allows us to capture imaging information beyond the visually recognizable level of a human being. Because of the anatomic characteristics and location, on-board target verification for radiation de...

Prediction of emergency department patient disposition based on natural language processing of triage notes.

International journal of medical informatics
BACKGROUND: Nursing triage documentation is the first free-form text data created at the start of an emergency department (ED) visit. These 1-3 unstructured sentences reflect the clinical impression of an experienced nurse and are key in gauging a pa...

The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual a...