AI pipeline uses uncertainty to triage brain tumor MRIs

Researchers have developed a novel pipeline for brain tumor MRI triage that leverages Monte Carlo Dropout and entropy-thresholding to assess model confidence. This approach aims to identify cases likely to be misclassified, enabling deferral to human radiologists. The system demonstrated strong discrimination with a macro-AUC of 0.994 and achieved high accuracy (0.962-0.964) on four-class brain tumor classification. By calibrating model outputs and deferring the most uncertain cases, the pipeline significantly improved accuracy on the remaining cases to approximately 0.98. AI

IMPACT Enhances reliability of AI in medical diagnostics by enabling trusted deferral of uncertain cases to human experts.

RANK_REASON Academic paper detailing a novel method for AI model uncertainty estimation and its application in medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Entropy-Thresholded Selective Prediction
• magnetic resonance imaging
• Monte Carlo Dropout
• ResNet-50
• ViT-B/16

• paper
• model release
• safety

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