AIMC Topic: Deep Learning

Clear Filters Showing 13561 to 13570 of 28423 articles

Functional Gait Assessment Using Manual, Semi-Automated and Deep Learning Approaches Following Standardized Models of Peripheral Nerve Injury in Mice.

Biomolecules
Objective: To develop a standardized model of stretch−crush sciatic nerve injury in mice, and to compare outcomes of crush and novel stretch−crush injuries using standard manual gait and sensory assays, and compare them to both semi-automated as well...

Deep learning for neural decoding in motor cortex.

Journal of neural engineering
. Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep...

Deep Learning-Based Medical Data Association Rules to Explore the Connectivity and Regulation Mechanism of miRNA-mRNA Network in Myocarditis.

Computational and mathematical methods in medicine
Acute, chronic myocarditis as myocardial localized or diffuse inflammation lesions is usually involving cardiac function in patients with severe adverse outcomes such as heart failure, sudden death, and no unified, but its pathogenesis clinical is ma...

Effectiveness of Artificial Intelligence (AI) in Improving Pupils' Deep Learning in Primary School Mathematics Teaching in Fujian Province.

Computational intelligence and neuroscience
Several primary school students in Fujian Province have perceived studying mathematics as challenging. To deal with this issue, computer technology advancements, specifically artificial intelligence (AI), present an opportunity to evaluate individual...

Intelligent Identification of Coal Crack in CT Images Based on Deep Learning.

Computational intelligence and neuroscience
Automatic segmentation of coal crack in CT images is of great significance for the establishment of digital cores. In addition, segmentation in this field remains challenging due to some properties of coal crack CT images: high noise, small targets, ...

Deep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC). However, CT-based AC is not available for most SPECT systems in clinical use, increases radiation exposure, and is impacted...

Training Deep Learning Models to Work on Multiple Devices by Cross-Domain Learning with No Additional Annotations.

Ophthalmology
PURPOSE: To create an unsupervised cross-domain segmentation algorithm for segmenting intraretinal fluid and retinal layers on normal and pathologic macular OCT images from different manufacturers and camera devices.

Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma.

Current eye research
PURPOSE: We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective...

Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: In nasal or sinonasal tumors, orbital invasion beyond periorbita by the tumor is one of the important criteria in the selection of the surgical procedure. We investigated the usefulness of the convolutional neural network (CNN)-based deep...

A pipeline for automated deep learning liver segmentation (PADLLS) from contrast enhanced CT exams.

Scientific reports
Multiple studies have created state-of-the-art liver segmentation models using Deep Convolutional Neural Networks (DCNNs) such as the V-net and H-DenseUnet. Oversegmentation however continues to be a problem. We set forth to address these limitations...