Risk-Calibrated Learning reduces critical error rates in medical AI by 20-92% across four imaging datasets by embedding a severity matrix into the optimization.
Beyond the mean: Distribution-aware loss functions for bimodal regression
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A dual GBDT error classifier reduces dangerous misclassifications by 12-34% on medical and animal image datasets with under 2% added latency.
citing papers explorer
-
Risk-Calibrated Learning: Minimizing Fatal Errors in Medical AI
Risk-Calibrated Learning reduces critical error rates in medical AI by 20-92% across four imaging datasets by embedding a severity matrix into the optimization.
-
Improving Model Safety by Targeted Error Correction
A dual GBDT error classifier reduces dangerous misclassifications by 12-34% on medical and animal image datasets with under 2% added latency.