AIMC Topic: Deep Learning

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Deep learning for whole-body medical image generation.

European journal of nuclear medicine and molecular imaging
BACKGROUND: Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and tr...

Suppression of artifact-generating echoes in cine DENSE using deep learning.

Magnetic resonance in medicine
PURPOSE: To use deep learning for suppression of the artifact-generating T -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time.

End-to-end deep learning for recognition of ploidy status using time-lapse videos.

Journal of assisted reproduction and genetics
PURPOSE: Our retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video.

Image-based cell phenotyping with deep learning.

Current opinion in chemical biology
A cell's phenotype is the culmination of several cellular processes through a complex network of molecular interactions that ultimately result in a unique morphological signature. Visual cell phenotyping is the characterization and quantification of ...

Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning.

Academic radiology
RATIONALE AND OBJECTIVES: The clinical prognosis of outpatients with coronavirus disease 2019 (COVID-19) remains difficult to predict, with outcomes including asymptomatic, hospitalization, intubation, and death. Here we determined the prognostic val...

Enhancing the X-Ray Differential Phase Contrast Image Quality With Deep Learning Technique.

IEEE transactions on bio-medical engineering
OBJECTIVE: The purpose of this work is to investigate the feasibility of using deep convolutional neural network (CNN) to improve the image quality of a grating-based X-ray differential phase contrast imaging (XPCI) system.

Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm.

Sensors (Basel, Switzerland)
The chicken industry, in which broiler chickens are bred, is the largest poultry industry in Taiwan. In a traditional poultry house, breeders must usually observe the health of the broilers in person on the basis of their breeding experience at regul...

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages.

Scientific reports
Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5-40 μm) stained with Prussian blue. Currently, users manually and sub...

Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets.

Nature communications
Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is...

Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging.

European radiology
OBJECTIVES: Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether RE has brain developmental delay. This study employed deep learning-based neuroanatomic biomarker t...