Well-Calibrated Regression Uncertainty in Medical Imaging with Deep Learning

Max-Heinrich Laves, Sontje Ihler, Jacob F. Fast, Lüder A. Kahrs, Tobias Ortmaier

Keywords: bayesian approximation, variational inference

TL;DR: We recalibrate underestimated predictive uncertainty for different regression tasks in medical imaging.

Abstract:

The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance.
We apply estimation of predictive uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show why predictive uncertainty is systematically underestimated.
We suggest using $ \sigma $ scaling with a single scalar value; a simple, yet effective calibration method for both aleatoric and epistemic uncertainty.
The performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-art convolutional network architectures.
In all experiments, $ \sigma $ scaling is able to reliably recalibrate predictive uncertainty.
It is easy to implement and maintains the accuracy.
Well-calibrated uncertainty in regression allows robust rejection of unreliable predictions or detection of out-of-distribution samples.

Track: full conference paper

Paper Type: both

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