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View article: Iterative Quality Control Strategies for Expert Medical Image Labeling
Iterative Quality Control Strategies for Expert Medical Image Labeling Open
Data quality is a key concern for artificial intelligence (AI) efforts that rely on crowdsourced data collection. In the domain of medicine in particular, labeled data must meet high quality standards, or the resulting AI may perpetuate bi…
View article: Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology Open
View article: Large-scale machine learning-based phenotyping significantly improves\n genomic discovery for optic nerve head morphology
Large-scale machine learning-based phenotyping significantly improves\n genomic discovery for optic nerve head morphology Open
Genome-wide association studies (GWAS) require accurate cohort phenotyping,\nbut expert labeling can be costly, time-intensive, and variable. Here we\ndevelop a machine learning (ML) model to predict glaucomatous optic nerve head\nfeatures…
View article: Improving Medical Annotation Quality to Decrease Labeling Burden Using Stratified Noisy Cross-Validation
Improving Medical Annotation Quality to Decrease Labeling Burden Using Stratified Noisy Cross-Validation Open
As machine learning has become increasingly applied to medical imaging data, noise in training labels has emerged as an important challenge. Variability in diagnosis of medical images is well established; in addition, variability in traini…
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View article: Identifying and mitigating low-quality labels for deep learning in glaucoma
Identifying and mitigating low-quality labels for deep learning in glaucoma Open
View article: Author Correction: Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program
Author Correction: Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program Open
View article: A Study of Feature-based Consensus Formation for Glaucoma Risk Assessment
A Study of Feature-based Consensus Formation for Glaucoma Risk Assessment Open
View article: Identifying glaucomatous optic nerve head features and glaucoma risk in fundus images at eye-care provider levels of accuracy using deep learning algorithms
Identifying glaucomatous optic nerve head features and glaucoma risk in fundus images at eye-care provider levels of accuracy using deep learning algorithms Open
View article: Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program Open
View article: Deep Learning to Assess Glaucoma Risk and Associated Features in Fundus Images.
Deep Learning to Assess Glaucoma Risk and Associated Features in Fundus Images. Open
View article: Deep Learning vs. Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a Real-World Nationwide Screening Program
Deep Learning vs. Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a Real-World Nationwide Screening Program Open
Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy. This study aims to validate one such algorithm on a large-scale clinical population, and compare the algorithm performance with tha…