SLA detects noisy labels task-agnostically by standardizing and aggregating validation losses across repeated cross-validation folds, generalizing hard-counting into a continuous estimator that outperforms baselines on fundus data.
Deep learning with noisy labels in medical prediction problems: a scoping review
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Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation
SLA detects noisy labels task-agnostically by standardizing and aggregating validation losses across repeated cross-validation folds, generalizing hard-counting into a continuous estimator that outperforms baselines on fundus data.