mlsanityData to 7 Oct 2026

A Benchmark of Medical Out of Distribution Detection

arXiv 2007.04250 ↗First posted 8 Jul 2020cs.LG cs.CV stat.MLcited by 40

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Abstract (arXiv)

Motivation: Deep learning models deployed for use on medical tasks can be equipped with Out-of-Distribution Detection (OoDD) methods in order to avoid erroneous predictions. However it is unclear which OoDD method should be used in practice. Specific Problem: Systems trained for one particular domain of images cannot be expected to perform accurately on images of a different domain. These images should be flagged by an OoDD method prior to diagnosis. Our approach: This paper defines 3 categories of OoD examples and benchmarks popular OoDD methods in three domains of medical imaging: chest X-ray, fundus imaging, and histology slides. Results: Our experiments show that despite methods yielding good results on some categories of out-of-distribution samples, they fail to recognize images close to the training distribution. Conclusion: We find a simple binary classifier on the feature representation has the best accuracy and AUPRC on average. Users of diagnostic tools which employ these OoDD methods should still remain vigilant that images very close to the training distribution yet not in it could yield unexpected results.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2020.

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Citing papers per year: 2021: 11; 2022: 10; 2023: 8; 2024: 5; 2025: 3; 2026: 3.

40 papers.

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  1. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  2. 2021Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditionscited by 68
  3. 2023Learning to Augment Distributions for Out-of-Distribution Detectioncited by 38
  4. 2021How is BERT surprised? Layerwise detection of linguistic anomaliescited by 24
  5. 2024Out-of-distribution Detection in Medical Image Analysis: A surveycited by 22
  6. 2021Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detectioncited by 21
  7. 2025GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generationcited by 15
  8. 2025OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detectioncited by 13
  9. 2024Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detectioncited by 12
  10. 2022Recommendations on test datasets for evaluating AI solutions in pathologycited by 8
  11. 2024Are We Ready for Out-of-Distribution Detection in Digital Pathology?cited by 8
  12. 2022CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AIcited by 7
28 more
  1. 2023Out-of-distribution detection algorithms for robust insect classificationcited by 7
  2. 2023A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1cited by 7
  3. 2021CVAD: A generic medical anomaly detector based on Cascade VAEcited by 6
  4. 2023Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentationcited by 6
  5. 2021An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applicationscited by 4
  6. 2023Confidence-Aware and Self-Supervised Image Anomaly Localisationcited by 4
  7. 2021CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rayscited by 3
  8. 2022Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approachcited by 3
  9. 2022Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detectioncited by 3
  10. 2023Redesigning Out-of-Distribution Detection on 3D Medical Imagescited by 3
  11. 2021Margin-Aware Intra-Class Novelty Identification for Medical Imagescited by 2
  12. 2023MLHOps: Machine Learning for Healthcare Operationscited by 2
  13. 2023Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitzcited by 2
  14. 2021Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Reviewcited by 1
  15. 2021Thinkback: Task-SpecificOut-of-Distribution Detectioncited by 1
  16. 2022A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networkscited by 1
  17. 2022DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measurescited by 1
  18. 2024Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Predictioncited by 1
  19. 2025Domain Feature Collapse: Implications for Out-of-Distribution Detection and Solutionscited by 1
  20. 2021Loss Estimators Improve Model Generalizationcited by 0
  21. 2021Out of distribution detection for skin and malaria imagescited by 0
  22. 2022Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcarecited by 0
  23. 2022Integrating Linguistic Theory and Neural Language Modelscited by 0
  24. 2022Solving Sample-Level Out-of-Distribution Detection on 3D Medical Imagescited by 0
  25. 2024ShiftedBronzes: Benchmarking and Analysis of Domain Fine-Grained Classification in Open-World Settingscited by 0
  26. 2026Beyond the Class Subspace: Teacher-Guided Training for Reliable Out-of-Distribution Detection in Single-Domain Modelscited by 0
  27. 2026CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Predictioncited by 0
  28. 2026Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detectioncited by 0