Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy¶
Krause. (2018). Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy.
Cited by¶
1 citation across 1 artifact.
Domain-specific¶
- Label Ambiguity
- Grading medical images is the textbook setting, because expert disagreement there is real and documented — ophthalmologists, for instance, are known to disagree substantially when grading diabetic-retinopathy severity from fundus photographs
Supported in partVerified against the work's full text
“there can be a fair amount of grader variability, with intergrader kappa scores ranging from 0.40 to 0.65 [1, 4–7].”
- Grading medical images is the textbook setting, because expert disagreement there is real and documented — ophthalmologists, for instance, are known to disagree substantially when grading diabetic-retinopathy severity from fundus photographs
Verification¶
Does it exist? Not checked yet. This work's DOI is recorded above but has not been resolved against an external catalogue, so nothing here confirms the work exists.
Does it back the claim? Read against the text for 1 of 1 citation: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:db1caaab8cda · see in the full table