{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/well-calibrated-regression-uncertainty-in","title":"Well-Calibrated Regression Uncertainty in Medical Imaging with Deep Learning","arxiv_id":null,"date":"2020-01-25","proceeding":"MIDL 2019 7","authors":["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.\nWe apply estimation of predictive uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show why predictive uncertainty is systematically underestimated.\nWe suggest using $ \\sigma $ scaling with a single scalar value; a simple, yet effective calibration method for both aleatoric and epistemic uncertainty.\nThe performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-art convolutional network architectures.\nIn all experiments, $ \\sigma $ scaling is able to reliably recalibrate predictive uncertainty.\nIt is easy to implement and maintains the accuracy.\nWell-calibrated uncertainty in regression allows robust rejection of unreliable predictions or detection of out-of-distribution samples.","url_abs":"https://openreview.net/forum?id=CecZ_0t79q","url_pdf":"https://openreview.net/pdf?id=CecZ_0t79q","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"well-calibrated-regression-uncertainty-in","repo_url":"https://github.com/mlaves/3doct-pose-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"well-calibrated-regression-uncertainty-in","repo_url":"https://github.com/mlaves/well-calibrated-regression-uncertainty","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}