AIMC Topic: Deep Learning

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Artificial intelligence in the diagnosis and management of hepatocellular carcinoma.

Journal of gastroenterology and hepatology
Despite recent improvements in therapeutic interventions, hepatocellular carcinoma is still associated with a poor prognosis in patients with an advanced disease at diagnosis. Recently, significant progress has been made in image recognition through ...

Radiomics and deep learning in liver diseases.

Journal of gastroenterology and hepatology
Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high-dimensional image-derived features and have the potential to exp...

Dual-wavelength interferogram decoupling method for three-frame generalized dual-wavelength phase-shifting interferometry based on deep learning.

Journal of the Optical Society of America. A, Optics, image science, and vision
In dual-wavelength interferometry, the key issue is how to efficiently retrieve the phases at each wavelength using the minimum number of wavelength-multiplexed interferograms. To address this problem, a new dual-wavelength interferogram decoupling m...

Using Virtual Simulation To Increase Deep Learning in Radiography Students.

Radiologic technology
PURPOSE: To discuss recent studies that validate the combination of traditional teaching and virtual simulation training in reducing common errors, enhancing students' confidence, improving their performance, and increasing deep learning.

Artificial Intelligence Algorithm for Screening Heart Failure with Reduced Ejection Fraction Using Electrocardiography.

ASAIO journal (American Society for Artificial Internal Organs : 1992)
Although heart failure with reduced ejection fraction (HFrEF) is a common clinical syndrome and can be modified by the administration of appropriate medical therapy, there is no adequate tool available to perform reliable, economical, early-stage scr...

PET/CT for Brain Amyloid: A Feasibility Study for Scan Time Reduction by Deep Learning.

Clinical nuclear medicine
PURPOSE: This study was to develop a convolutional neural network (CNN) model with a residual learning framework to predict the full-time 18F-florbetaben (18F-FBB) PET/CT images from corresponding short-time scans.

Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text.