mlsanityData to 14 Oct 2026

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

arXiv 2503.16247 ↗First posted 20 Mar 2025cs.CV cs.LGcited by 14

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Abstract (arXiv)

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, near-OOD, 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 OOD detection 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.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 45 of its 78 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

45 of 78 references.

References · 45

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33 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 9.

14 papers.

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  1. 2025OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Taskscited by 8
  2. 2025Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlookcited by 3
  3. 2026When Adaptation Hurts: Connecting Representational Drift to OOD Failures in MedSAM Fine-Tuningcited by 1
  4. 2025DIsoN: Decentralized Isolation Networks for Out-of-Distribution Detection in Medical Imagingcited by 0
  5. 2025Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasetscited by 0
  6. 2025A Systematic Analysis of Out-of-Distribution Detection Under Representation and Training Paradigm Shiftscited by 0
  7. 2026The Invisible Gorilla Effect in Out-of-distribution Detectioncited by 0
  8. 2026CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Predictioncited by 0
  9. 2026PROTON: Prototype-Based Test-Time Online OOD Detection for Medical VLMscited by 0
  10. 2026MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoringcited by 0
  11. 2026Mitigating Class-Tail Undercoverage in Medical Vision-Language Models under Clinical Shiftcited by 0
  12. 2026Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detectioncited by 0
2 more
  1. 2026Layer Selection in VLMs for Zero-Shot OOD Detection via Multi-Resolution Entropy Estimationcited by 0
  2. 2026ReMaD: Tuning-free Domain Adaptation for Classification and Out-of-Distribution Detectioncited by 0