Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging

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

The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show that predictive uncertainty is systematically underestimated. We apply $ \sigma $ scaling with a single scalar value; a simple, yet effective calibration method for both types of 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 our 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. Our source code is available at github.com/mlaves/well-calibrated-regression-uncertainty

Organisationseinheit(en)
Institut für Mechatronische Systeme
Externe Organisation(en)
Technische Universität Hamburg-Harburg (TUHH)
Medizinische Hochschule Hannover (MHH)
University of Toronto
Typ
Artikel
Journal
The Journal of Machine Learning for Biomedical Imaging (MELBA)
Band
MIDL 2020
Seiten
1-26
Anzahl der Seiten
26
ISSN
2766-905X
Publikationsdatum
28.04.2021
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
Elektronische Version(en)
https://arxiv.org/abs/2104.12376 (Zugang: Offen)