AIMC Topic: Photography

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Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders.

Journal of diabetes research Dec 15, 2020
OBJECTIVE: To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR.
Macular Edema Prevalence Image Interpretation, Computer-Assisted Reproducibility of Results Thailand Photography Humans Aged Severity of Illness Index National Health Programs Diabetic Retinopathy Male Deep Learning Longitudinal Studies Female Middle Aged Predictive Value of Tests Incidence Fundus Oculi Cell Proliferation Mass Screening
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Simultaneous Diagnosis of Severity and Features of Diabetic Retinopathy in Fundus Photography Using Deep Learning.

IEEE journal of biomedical and health informatics Dec 4, 2020
Deep learning methods for diabetic retinopathy (DR) diagnosis are usually criticized as being lack of interpretability in the diagnostic result, thus limiting their application in clinic. Simultaneous prediction of DR related features during the DR s...
Image Interpretation, Computer-Assisted Female Aged Photography Aged, 80 and over Humans Young Adult Adolescent Diabetic Retinopathy Adult Child ROC Curve Male Middle Aged Deep Learning
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Hard Attention Net for Automatic Retinal Vessel Segmentation.

IEEE journal of biomedical and health informatics Dec 4, 2020
Automated retinal vessel segmentation is among the most significant application and research topics in ophthalmologic image analysis. Deep learning based retinal vessel segmentation models have attracted much attention in the recent years. However, c...
Humans Databases, Factual Fundus Oculi Diagnostic Techniques, Ophthalmological Photography Retinal Vessels Deep Learning Child ROC Curve Image Processing, Computer-Assisted
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Estimating visual field loss from monoscopic optic disc photography using deep learning model.

Scientific reports Dec 3, 2020
Visual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test-retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of sta...
Female Male Middle Aged Deep Learning Humans Algorithms Visual Fields Photography ROC Curve Models, Biological Optic Disk
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Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning.

Translational vision science & technology Dec 2, 2020
PURPOSE: To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis that reveals its essential pathological features via fundus photographs.
Deep Learning Mice Uveitis Fundus Oculi Animals Diagnostic Techniques, Ophthalmological Photography
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Generating photo-realistic training data to improve face recognition accuracy.

Neural networks : the official journal of the International Neural Network Society Nov 27, 2020
Face recognition has become a widely adopted biometric in forensics, security and law enforcement thanks to the high accuracy achieved by systems based on convolutional neural networks (CNNs). However, to achieve good performance, CNNs need to be tra...
Photography Facial Recognition Humans Neural Networks, Computer Machine Learning Automated Facial Recognition
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Predicting the risk of developing diabetic retinopathy using deep learning.

The Lancet. Digital health Nov 26, 2020
BACKGROUND: Diabetic retinopathy screening is instrumental to preventing blindness, but scaling up screening is challenging because of the increasing number of patients with all forms of diabetes. We aimed to create a deep-learning system to predict ...
Kaplan-Meier Estimate Diagnostic Techniques, Ophthalmological Photography Risk Assessment Deep Learning ROC Curve Area Under Curve Aged Male Middle Aged Humans Diabetic Retinopathy Prognosis Female Reproducibility of Results
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Identifying gross post-mortem organ images using a pre-trained convolutional neural network.

Journal of forensic sciences Oct 26, 2020
Identifying organs/tissue and pathology on radiological and microscopic images can be performed using convolutional neural networks (CNN). However, there are scant studies on applying CNN to post-mortem gross images of visceral organs. This proof-of-...
Spleen Photography Forensic Pathology Image Processing, Computer-Assisted Humans Kidney Liver Neural Networks, Computer Myocardium Brain Lung
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Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs.

Scientific reports Oct 20, 2020
To demonstrate the identification of corneal diseases using a novel deep learning algorithm. A novel hierarchical deep learning network, which is composed of a family of multi-task multi-label learning classifiers representing different levels of eye...
Sensitivity and Specificity Deep Learning Photography Corneal Diseases Humans Algorithms Prospective Studies Retrospective Studies
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Sex judgment using color fundus parameters in elementary school students.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie Oct 16, 2020
PURPOSES: Recently, artificial intelligence has been used to determine sex using fundus photographs alone. We had earlier reported that sex can be distinguished using known factors obtained from color fundus photography (CFP) in adult eyes. However, ...
Humans Artificial Intelligence Female Male Fundus Oculi Students Photography Infant, Newborn Tomography, Optical Coherence Judgment Schools Adult Cross-Sectional Studies
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