mlsanityData to 7 Oct 2026

Deep Learning for Medical Anomaly Detection -- A Survey

arXiv 2012.02364 ↗First posted 4 Dec 2020cs.LG cs.CV eess.IVcited by 127

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Abstract (arXiv)

Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structured organisation of these diverse research applications such that their advantages and limitations can be studied. The principal aim of this survey is to provide a thorough theoretical analysis of popular deep learning techniques in medical anomaly detection. In particular, we contribute a coherent and systematic review of state-of-the-art techniques, comparing and contrasting their architectural differences as well as training algorithms. Furthermore, we provide a comprehensive overview of deep model interpretation strategies that can be used to interpret model decisions. In addition, we outline the key limitations of existing deep medical anomaly detection techniques and propose key research directions for further investigation.

Key facts. Cited by 127 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 11; 2023: 22; 2024: 29; 2025: 36; 2026: 25.

127 papers.

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  1. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 270
  2. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  3. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
  4. 2023EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latenciescited by 157
  5. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  6. 2025AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIPcited by 72
  7. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  8. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  9. 2025Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistantcited by 54
  10. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  11. 2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
  12. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
38 more
  1. 2023CLIP in Medical Imaging: A Surveycited by 40
  2. 2021A Survey on Anomaly Detection for Technical Systems using LSTM Networkscited by 27
  3. 2023Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Modelcited by 25
  4. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  5. 2024AD-LLM: Benchmarking Large Language Models for Anomaly Detectioncited by 25
  6. 2024Out-of-distribution Detection in Medical Image Analysis: A surveycited by 22
  7. 2024Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detectioncited by 19
  8. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 15
  9. 2023LafitE: Latent Diffusion Model with Feature Editing for Unsupervised Multi-class Anomaly Detectioncited by 13
  10. 2024Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Imagescited by 13
  11. 2022Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANscited by 11
  12. 2022Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentcited by 11
  13. 2024Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETcited by 10
  14. 2024Anomaly Detection Based on Isolation Mechanisms: A Surveycited by 10
  15. 2025MadCLIP: Few-shot Medical Anomaly Detection with CLIPcited by 10
  16. 2024Learning Multi-view Anomaly Detection with Efficient Adaptive Selectioncited by 9
  17. 2023Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptorscited by 8
  18. 2023Synthetic Data in Healthcarecited by 8
  19. 2023Anomaly Detection with Variance Stabilized Density Estimationcited by 8
  20. 2023Model Selection of Anomaly Detectors in the Absence of Labeled Validation Datacited by 8
  21. 2025Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Reviewcited by 8
  22. 2025PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomaliescited by 8
  23. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  24. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  25. 2023High-Fidelity Zero-Shot Texture Anomaly Localization Using Feature Correspondence Analysiscited by 7
  26. 2024A SAM-guided Two-stream Lightweight Model for Anomaly Detectioncited by 7
  27. 2025Towards Accurate Unified Anomaly Segmentationcited by 7
  28. 2025AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detectioncited by 7
  29. 2021CVAD: A generic medical anomaly detector based on Cascade VAEcited by 6
  30. 2022Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Codingcited by 6
  31. 2023Deep Learning Approaches for Seizure Video Analysis: A Reviewcited by 6
  32. 2024Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detectioncited by 5
  33. 2024ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shiftcited by 5
  34. 2026Towards One-for-All Anomaly Detection for Tabular Datacited by 5
  35. 2022Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Predictioncited by 4
  36. 2022Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detectioncited by 4
  37. 2022Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networkscited by 4
  38. 2024Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectorscited by 4

50 most cited of 127 All 127 (JSON)