AIMC Topic: Deep Learning

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A Survey of Deep Learning Techniques for Underwater Image Classification.

IEEE transactions on neural networks and learning systems
In recent years, there has been an enormous interest in using deep learning to classify underwater images to identify various objects, such as fishes, plankton, coral reefs, seagrass, submarines, and gestures of sea divers. This classification is ess...

Artificial intelligence to analyze magnetic resonance imaging in rheumatology.

Joint bone spine
Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance im...

Differentiating spinal pathologies by deep learning approach.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Spinal pathologies are diverse in nature and, excluding trauma and degenerative diseases, includes infectious, neoplastic (either extradural or intradural), and inflammatory conditions. The preoperative diagnosis is made with clin...

Spatio-temporal fusion of meteorological factors for multi-site PM2.5 prediction: A deep learning and time-variant graph approach.

Environmental research
In the field of environmental science, traditional methods for predicting PM2.5 concentrations primarily focus on singular temporal or spatial dimensions. This approach presents certain limitations when it comes to deeply mining the joint influence o...

Enhancing the performance of premature ventricular contraction detection in unseen datasets through deep learning with denoise and contrast attention module.

Computers in biology and medicine
Premature ventricular contraction (PVC) is a common and harmless cardiac arrhythmia that can be asymptomatic or cause palpitations and chest pain in rare instances. However, frequent PVCs can lead to more serious arrhythmias, such as atrial fibrillat...

Enhancing percutaneous coronary intervention with heuristic path planning and deep-learning-based vascular segmentation.

Computers in biology and medicine
Percutaneous coronary intervention (PCI) is a minimally invasive technique for treating vascular diseases. PCI requires precise and real-time visualization and guidance to ensure surgical safety and efficiency. Existing mainstream guiding methods rel...

Contour subregion error detection methodology using deep learning auto-segmentation.

Medical physics
BACKGROUND: Inaccurate manual organ delineation is one of the high-risk failure modes in radiation treatment. Numerous automated contour quality assurance (QA) systems have been developed to assess contour acceptability; however, manual inspection of...

Complexities of deep learning-based undersampled MR image reconstruction.

European radiology experimental
Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction method...

Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE: To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance.