OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection

  • The growing reliance on Artificial Intelligence (AI) in critical domains such as healthcare demands robust mechanisms to ensure the trustworthiness of these systems, especially when faced with unexpected or anomalous inputs. This paper introduces the Open Medical Imaging Benchmarks for Out-Of-Distribution Detection (OpenMIBOOD), a comprehensive framework for evaluating out-of-distribution (OOD)The growing reliance on Artificial Intelligence (AI) in critical domains such as healthcare demands robust mechanisms to ensure the trustworthiness of these systems, especially when faced with unexpected or anomalous inputs. This paper introduces the Open Medical Imaging Benchmarks for Out-Of-Distribution Detection (OpenMIBOOD), a comprehensive framework for evaluating out-of-distribution (OOD) detection methods specifically in medical imaging contexts. OpenMIBOOD includes three benchmarks from diverse medical domains, encompassing 14 datasets divided into covariate-shifted in-distribution, nearOOD, and far-OOD categories. We evaluate 24 post-hoc methods across these benchmarks, providing a standardized reference to advance the development and fair comparison of OODdetection methods. Results reveal that findings from broad-scale OOD benchmarks in natural image domains do not translate to medical applications, underscoring the critical need for such benchmarks in the medical field. By mitigating the risk of exposing AI models to inputs outside their training distribution, OpenMIBOOD aims to support the advancement of reliable and trustworthy AI systems in healthcare. The repository is available at https://github.com/remic-othr/OpenMIBOOD.show moreshow less

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics
Metadaten
Author:Max GutbrodORCiD, David RauberORCiD, Danilo Weber NunesORCiD, Christoph PalmORCiDGND
URL / DOI:https://openaccess.thecvf.com/content/CVPR2025/html/Gutbrod_OpenMIBOOD_Open_Medical_Imaging_Benchmarks_for_Out-Of-Distribution_Detection_CVPR_2025_paper.html
DOI:https://doi.org/10.1109/CVPR52734.2025.02410
ISBN:979-8-3315-4364-8
Parent Title (English):2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10.-17. June 2025, Nashville
Publisher:IEEE
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2025
Release Date:2025/08/26
Tag:Benchmark testing; Reliability; Trustworthiness; out-of-distribution
First Page:25874
Last Page:25886
Note:
Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/8059
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Informatik und Mathematik / Labor Regensburg Medical Image Computing (ReMIC)
Begutachtungsstatus:peer-reviewed
research focus:Gesundheit und Soziales
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG
Einverstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.