mlsanityData to 14 Oct 2026

Out-of-distribution Detection in Medical Image Analysis: A survey

arXiv 2404.18279 ↗First posted 28 Apr 2024cs.CVcited by 22

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Abstract (arXiv)

Computer-aided diagnostics has benefited from the development of deep learning-based computer vision techniques in these years. Traditional supervised deep learning methods assume that the test sample is drawn from the identical distribution as the training data. However, it is possible to encounter out-of-distribution samples in real-world clinical scenarios, which may cause silent failure in deep learning-based medical image analysis tasks. Recently, research has explored various out-of-distribution (OOD) detection situations and techniques to enable a trustworthy medical AI system. In this survey, we systematically review the recent advances in OOD detection in medical image analysis. We first explore several factors that may cause a distributional shift when using a deep-learning-based model in clinic scenarios, with three different types of distributional shift well defined on top of these factors. Then a framework is suggested to categorize and feature existing solutions, while the previous studies are reviewed based on the methodology taxonomy. Our discussion also includes evaluation protocols and metrics, as well as the challenge and a research direction lack of exploration.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 68 of its 107 references that are in the map. First posted on arXiv 28 Apr 2024.

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68 of 107 references.

References · 68

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
  5. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  6. 2017On Calibration of Modern Neural Networkscited by 4,960
  7. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  8. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  9. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  10. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  11. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  12. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
38 more
  1. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  2. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  3. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  4. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  5. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  6. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  7. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  8. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  9. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  10. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 776
  11. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  12. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 700
  13. 2018Predictive Uncertainty Estimation via Prior Networkscited by 557
  14. 2015Towards Open Set Deep Networkscited by 538
  15. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 514
  16. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  17. 2021Revisiting the Calibration of Modern Neural Networkscited by 390
  18. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  19. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 339
  20. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  21. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  22. 2021Exploring the Limits of Out-of-Distribution Detectioncited by 283
  23. 2018Learning Confidence for Out-of-Distribution Detection in Neural Networkscited by 245
  24. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  25. 2019Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentationcited by 163
  26. 2021Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIPcited by 152
  27. 2021Query2Label: A Simple Transformer Way to Multi-Label Classificationcited by 128
  28. 2020General Multi-label Image Classification with Transformerscited by 127
  29. 2020Deep Learning for Medical Anomaly Detection -- A Surveycited by 127
  30. 2019Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustnesscited by 104
  31. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  32. 2022Denoising diffusion models for out-of-distribution detectioncited by 104
  33. 2021Can multi-label classification networks know what they don't know?cited by 100
  34. 2019Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classificationcited by 79
  35. 2021Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditionscited by 68
  36. 2020Post-hoc Uncertainty Calibration for Domain Drift Scenarioscited by 61
  37. 2021Applications of Deep Learning in Fundus Images: A Reviewcited by 59
  38. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59

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Citing papers per year: 2024: 2; 2025: 11; 2026: 9.

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  1. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  2. 2025OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detectioncited by 14
  3. 2025NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRIcited by 13
  4. 2025CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveragingcited by 9
  5. 2024Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processescited by 6
  6. 2025Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Surveycited by 6
  7. 2025A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categoriescited by 6
  8. 2025Delving into Out-of-Distribution Detection with Medical Vision-Language Modelscited by 5
  9. 2025NERO: Explainable Out-of-Distribution Detection with Neuron-level Relevancecited by 3
  10. 2025Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detectioncited by 1
  11. 2025Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoringcited by 1
  12. 2026MahaVar: OOD Detection via Class-wise Mahalanobis Distance Variance under Neural Collapsecited by 1
10 more
  1. 2025Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasoundcited by 0
  2. 2025Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasetscited by 0
  3. 2026Ranked Activation Shift for Post-Hoc Out-of-Distribution Detectioncited by 0
  4. 2026MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoringcited by 0
  5. 2026MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correctioncited by 0
  6. 2026MMIR-TCM: Memory-Integrated Multimodal Inference and Retrieval for TCM Clinical Decision Supportcited by 0
  7. 2026Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoringcited by 0
  8. 2026VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentationcited by 0
  9. 2026Layer Selection in VLMs for Zero-Shot OOD Detection via Multi-Resolution Entropy Estimationcited by 0
  10. 2026Local Sparsity Enables Unsupervised LLM Safety Detectioncited by 0