Computational intelligence and neuroscience
Mar 19, 2022
Breast cancer is a dangerous disease with a high morbidity and mortality rate. One of the most important aspects in breast cancer treatment is getting an accurate diagnosis. Machine-learning (ML) and deep learning techniques can help doctors in makin...
The human-machine interface (HMI) has been studied for robot teleoperation with the aim of empowering people who experience motor disabilities to increase their interaction with the physical environment. The challenge of an HMI for robot control is t...
Computational and mathematical methods in medicine
Mar 19, 2022
Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work ex...
Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to '...
Acta radiologica (Stockholm, Sweden : 1987)
Mar 18, 2022
BACKGROUND: A high false-positive rate remains a technical glitch hindering the broad spectrum of application of deep-learning-based diagnostic tools in routine radiological practice from assisting in diagnosing rib fractures.
OBJECTIVES: To assess the diagnostic accuracy of an automated algorithm to detect left ventricular (LV) dilatation on non-ECG gated CT, using cardiac magnetic resonance (CMR) as reference standard.
Oral surgery, oral medicine, oral pathology and oral radiology
Mar 18, 2022
OBJECTIVE: This study aimed to evaluate a deep learning (DL) system using convolutional neural networks (CNNs) for automatic detection of caries on bitewing radiographs.
Algorithms that automatically identify nodular patterns in chest X-ray (CXR) images could benefit radiologists by reducing reading time and improving accuracy. A promising approach is to use deep learning, where a deep neural network (DNN) is trained...
Analysis of epitranscriptomic RNA modifications by deep sequencing-based approaches brings an essential contribution to the general knowledge on their precise locations and relative stoichiometry in cellular RNAs. To reveal RNA modifications, several...
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