Empirical comparison of deep ensembles and Monte Carlo dropout with GNLL and MSE losses, plus recalibration, shows DE and recalibrated GNLL perform best for predictive robustness and uncertainty calibration in PPG-based BP estimation under domain shift.
Videocad: an uncertainty-driven neural network for coronary artery disease screening from facial videos.IEEE Transactions on Instrumentation and Measurement, 72:1–12, 2022
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Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
Empirical comparison of deep ensembles and Monte Carlo dropout with GNLL and MSE losses, plus recalibration, shows DE and recalibrated GNLL perform best for predictive robustness and uncertainty calibration in PPG-based BP estimation under domain shift.