AIMC Topic: Deep Learning

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Contrast Media Reduction in Computed Tomography With Deep Learning Using a Generative Adversarial Network in an Experimental Animal Study.

Investigative radiology
OBJECTIVE: This feasibility study aimed to use optimized virtual contrast enhancement through generative adversarial networks (GAN) to reduce the dose of iodine-based contrast medium (CM) during abdominal computed tomography (CT) in a large animal mo...

FOD-Net: A deep learning method for fiber orientation distribution angular super resolution.

Medical image analysis
Mapping the human connectome using fiber-tracking permits the study of brain connectivity and yields new insights into neuroscience. However, reliable connectome reconstruction using diffusion magnetic resonance imaging (dMRI) data acquired by widely...

FM-Net: Deep Learning Network for the Fundamental Matrix Estimation from Biplanar Radiographs.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The fundamental matrix estimation is a classic problem in computer vision. The traditional algorithms require high-precision correspondences. However, correspondences in biplanar radiographs are difficult to match accurately...

Deep learning in food science: An insight in evaluating Pickering emulsion properties by droplets classification and quantification via object detection algorithm.

Advances in colloid and interface science
Understanding the complicated emulsion microstructures by microscopic images will help to further elaborate their mechanisms and relevance. The formidable goal of the classification and quantification of emulsion microstructure appears difficult to a...

SE-BLTCNN: A channel attention adapted deep learning model based on PSSM for membrane protein classification.

Computational biology and chemistry
Membrane protein classification is a key to inferring the function of uncharacterized membrane protein. To get around the time-consuming and expensive biochemical experiments in the wet lab, there has been a lot of research focusing on developing fas...

Deep learning-based framework for segmentation of multiclass rib fractures in CT utilizing a multi-angle projection network.

International journal of computer assisted radiology and surgery
PURPOSE: Clinical rib fracture diagnosis via computed tomography (CT) screening has attracted much attention in recent years. However, automated and accurate segmentation solutions remain a challenging task due to the large sets of 3D CT data to deal...

Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database.

Scientific reports
Accumulation of beta-amyloid in the brain and cognitive decline are considered hallmarks of Alzheimer's disease. Knowing from previous studies that these two factors can manifest in the retina, the aim was to investigate whether a deep learning metho...

Enabling Legal Risk Management Model for International Corporation with Deep Learning and Self Data Mining.

Computational intelligence and neuroscience
In uncertain times, risk management is critical in keeping companies from acting rashly and wrongly, allowing them to become more flexible and resilient. International cooperative production project investment and operational risks are different from...

Deep learning to enable color vision in the dark.

PloS one
Humans perceive light in the visible spectrum (400-700 nm). Some night vision systems use infrared light that is not perceptible to humans and the images rendered are transposed to a digital display presenting a monochromatic image in the visible spe...

Correlation of choroidal thickness with age in healthy subjects: automatic detection and segmentation using a deep learning model.

International ophthalmology
PROPOSE: The proposed deep learning model with a mask region-based convolutional neural network (Mask R-CNN) can predict choroidal thickness automatically. Changes in choroidal thickness with age can be detected with manual measurements. In this stud...