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

arXiv: https://arxiv.org/abs/2404.18279 · HTML: https://mlsanity.com/paper/2404.18279/

## 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.

## Builds on (most cited first, 30 of 68)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://mlsanity.com/paper/2202.09778/) (2022)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1906.12340/) (2019)
- [Scaling Out-of-Distribution Detection for Real-World Settings](https://mlsanity.com/paper/1911.11132/) (2019)
- [Revisiting the Calibration of Modern Neural Networks](https://mlsanity.com/paper/2106.07998/) (2021)
- [Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples](https://mlsanity.com/paper/1711.09325/) (2017)

## Built on by (most cited first, 22 of 22)

- [Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey](https://mlsanity.com/paper/2407.21794/) (2024)
- [OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detection](https://mlsanity.com/paper/2503.16247/) (2025)
- [NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI](https://mlsanity.com/paper/2505.14064/) (2025)
- [CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging](https://mlsanity.com/paper/2503.00325/) (2025)
- [Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes](https://mlsanity.com/paper/2412.20918/) (2024)
- [Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Survey](https://mlsanity.com/paper/2505.02448/) (2025)
- [A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories](https://mlsanity.com/paper/2509.17046/) (2025)
- [Delving into Out-of-Distribution Detection with Medical Vision-Language Models](https://mlsanity.com/paper/2503.01020/) (2025)
- [NERO: Explainable Out-of-Distribution Detection with Neuron-level Relevance](https://mlsanity.com/paper/2506.15404/) (2025)
- [Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection](https://mlsanity.com/paper/2508.07923/) (2025)
- [Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoring](https://mlsanity.com/paper/2510.01683/) (2025)
- [MahaVar: OOD Detection via Class-wise Mahalanobis Distance Variance under Neural Collapse](https://mlsanity.com/paper/2605.14413/) (2026)
- [Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasound](https://mlsanity.com/paper/2509.18326/) (2025)
- [Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasets](https://mlsanity.com/paper/2511.03855/) (2025)
- [Ranked Activation Shift for Post-Hoc Out-of-Distribution Detection](https://mlsanity.com/paper/2604.08572/) (2026)
- [MaRS: Robust Out-of-Distribution Detection via Mahalanobis Residual Scoring](https://mlsanity.com/paper/2606.22649/) (2026)
- [MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction](https://mlsanity.com/paper/2606.25651/) (2026)
- [MMIR-TCM: Memory-Integrated Multimodal Inference and Retrieval for TCM Clinical Decision Support](https://mlsanity.com/paper/2607.01814/) (2026)
- [Cross-Contextual Vision-Language Adaptation with LoRA for Personalized Severe Adverse Event Detection in Clinical Wound Monitoring](https://mlsanity.com/paper/2607.05625/) (2026)
- [VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation](https://mlsanity.com/paper/2608.10903/) (2026)
- [Layer Selection in VLMs for Zero-Shot OOD Detection via Multi-Resolution Entropy Estimation](https://mlsanity.com/paper/2609.08524/) (2026)
- [Local Sparsity Enables Unsupervised LLM Safety Detection](https://mlsanity.com/paper/2609.20129/) (2026)

## 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.