AIMC Topic: Deep Learning

Clear Filters Showing 27121 to 27130 of 28423 articles

Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Rheumatic heart disease (RHD) affects an estimated 39 million people worldwide and is the most common acquired heart disease in children and young adults. Echocardiograms are the gold standard for diagnosis of RHD, but there is a shortage ...

Clinically relevant pretraining is all you need.

Journal of the American Medical Informatics Association : JAMIA
Clinical notes present a wealth of information for applications in the clinical domain, but heterogeneity across clinical institutions and settings presents challenges for their processing. The clinical natural language processing field has made stri...

Deep learning and the electrocardiogram: review of the current state-of-the-art.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for ...

DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology.

Plant physiology
The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual an...

Histograms of Frequency-Intensity Distribution Deep Learning to Predict the Seizure Liability of Drugs in Electroencephalography.

Toxicological sciences : an official journal of the Society of Toxicology
Detection of seizures as well as that of seizure auras is effective in improving the predictive accuracy of seizure liability of drugs. Whereas electroencephalography has been known to be effective for the detection of seizure liability, no establish...

Automatic Localization of the Scleral Spur Using Deep Learning and Ultrasound Biomicroscopy.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop a convolutional neural network (CNN) for automated localization of the scleral spur in ultrasound biomicroscopy (UBM) images of open-angle eyes.

Deep Learning for Automated Diabetic Retinopathy Screening Fused With Heterogeneous Data From EHRs Can Lead to Earlier Referral Decisions.

Translational vision science & technology
PURPOSE: Fundus images are typically used as the sole training input for automated diabetic retinopathy (DR) classification. In this study, we considered several well-known DR risk factors and attempted to improve the accuracy of DR screening.

Closing the gap between single-unit and neural population codes: Insights from deep learning in face recognition.

Journal of vision
Single-unit responses and population codes differ in the "read-out" information they provide about high-level visual representations. Diverging local and global read-outs can be difficult to reconcile with in vivo methods. To bridge this gap, we stud...

[Deep Learning-Based Cardiac Imaging Data Measurement and Its Application in Diagnosis of Sudden Cardiac Death].

Fa yi xue za zhi
In the field of forensic medicine, diagnosis of sudden cardiac death is limited by subjective factors and manual measurement methods, so some parameters may have estimation deviation or measurement deviation. As postmortem CT imaging plays a more and...