Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
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Summary
Adjudication reduces the errors in DR grading by using a small number of adjudicated consensus grades as a tuning dataset and higher-resolution images as input, and to train an improved automated algorithm for DR grading.
- Type
- article
- Published
- 2017-10-04
- Cited by
- 517
- References
- 32
- Access
- Open access
- OpenAlex
- https://openalex.org/W2762741128
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3964573
Keywords
Medicine, Adjudication, Grading (engineering), Diabetic retinopathy, Kappa
References
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- ImageNet Large Scale Visual Recognition Challenge
- Interobserver agreement in the interpretation of single-field digital fundus images for diabetic retinopathy screening.
- Diagnostic Concordance Among Pathologists Interpreting Breast Biopsy Specimens
- Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs
- Rethinking the Inception Architecture for Computer Vision
- Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
- Concordance in diagnosis of diabetic retinopathy by fundus photography between retina specialists and a standardized reading center. Mexico City Diabetes Study Retinopathy Group.
- [International clinical diabetic retinopathy disease severity scale].
- Facts and Figures Concerning the Human Retina -- Webvision: The Organization of the Retina and Visual System
- A Practical Manual of Diabetic Retinopathy Management
Cited by
- Comparison of automated and expert human grading of diabetic retinopathy using smartphone-based retinal photography
- Deep learning applications in ophthalmology
- Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
- Phronesis of AI in radiology: Superhuman meets natural stupidity
- Classification of crystallization outcomes using deep convolutional neural networks
- Direct Uncertainty Prediction with Applications to Healthcare
- Sentiment Analysis Based on Deep Learning and Its Application in Screening for Perinatal Depression
- A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training
- Artificial intelligence in retina.
- Application of artificial intelligence in ophthalmology.
- Artificial intelligence in healthcare
- Deep Learning vs. Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a Real-World Nationwide Screening Program
- Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
- Artificial intelligence and deep learning in ophthalmology
- Bildgebung der diabetischen Retinopathie
- Resolvable vs. Irresolvable Disagreement
- Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review
- Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy
- DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs
- Klinische Stadieneinteilung der diabetischen Retinopathie
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