A unified inference-time augmentation framework using 13 methods with Bayesian optimization improves AUROC up to 8.5% and AUPRC up to 10.6% for PPG AF detection on five datasets with over 400 patients.
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.
citing papers explorer
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A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection
A unified inference-time augmentation framework using 13 methods with Bayesian optimization improves AUROC up to 8.5% and AUPRC up to 10.6% for PPG AF detection on five datasets with over 400 patients.
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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.