A unified inference-time augmentation framework with 13 methods and Bayesian-optimized parameters improves AUROC up to 8.5% and reduces false positives in PPG-based AF detection across five datasets.
BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging,
2 Pith papers cite this work. Polarity classification is still indexing.
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MC Dropout yields strong global uncertainty-error alignment in brain tumor segmentation yet reveals region-specific miscalibration on enhancing tumor that standard metrics miss.
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Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation
MC Dropout yields strong global uncertainty-error alignment in brain tumor segmentation yet reveals region-specific miscalibration on enhancing tumor that standard metrics miss.