Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2020). On theSindex it has a DataRank of 0.425. It has been cited 16 times.
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›Methodology & internals
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Context only (not used in score)- Has DOI
- Open Access
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DataRank Breakdown
Base Score Contribution
0.425
From this paper's citation signal
Citation Network Contribution
0
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Learn more about DataRank methodology →Authors (11)
National Science Foundation
Grant: 1524535
III: Small: Data Analysis in the Cloud with Guaranteed and Explainable Performance
National Institutes of Health
Grant: 5R35GM128638-03
Opening the Black Box of Machine Learning Models
National Institutes of Health
Grant: 5R35GM128638-05
Opening the Black Box of Machine Learning Models
National Institutes of Health
Grant: 1K23EY029246-01
Epidemiology and clinical outcomes of diabetic macular edema
National Science Foundation
Grant: 1535565
AitF: FULL: Query Processing with Optimal Communication Cost
Fields of Study
Keywords
Sustainable Development Goals