mlsanityData to 7 Oct 2026

Unsupervised Anomaly Localization using Variational Auto-Encoders

arXiv 1907.02796 ↗First posted 4 Jul 2019cs.LG eess.IV stat.MLcited by 73

Not on the map. Why?

Abstract (arXiv)

An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning and especially Variational Auto-Encoders (VAEs) have shown great potential in the unsupervised learning of data distributions. In principle, this allows for such a check and even the localization of parts in the image that are most suspicious. Currently, however, the reconstruction-based localization by design requires adjusting the model architecture to the specific problem looked at during evaluation. This contradicts the principle of building assumption-free models. We propose complementing the localization part with a term derived from the Kullback-Leibler (KL)-divergence. For validation, we perform a series of experiments on FashionMNIST as well as on a medical task including >1000 healthy and >250 brain tumor patients. Results show that the proposed formalism outperforms the state of the art VAE-based localization of anomalies across many hyperparameter settings and also shows a competitive max performance.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jul 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 14; 2023: 16; 2024: 16; 2025: 12; 2026: 6.

73 papers.

Sort:

By area:

  1. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  2. 2022Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysiscited by 62
  3. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  4. 2023Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Modelscited by 48
  5. 2021Anomaly Detection in Medical Imaging -- A Mini Reviewcited by 44
  6. 2024Diffusion Models with Implicit Guidance for Medical Anomaly Detectioncited by 26
  7. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  8. 2023Unsupervised Pathology Detection: A Deep Dive Into the State of the Artcited by 24
  9. 2023The role of noise in denoising models for anomaly detection in medical imagescited by 23
  10. 2022Unsupervised Anomaly Localization with Structural Feature-Autoencoderscited by 22
  11. 2021Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Imagescited by 18
  12. 2023Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detectioncited by 18
38 more
  1. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIscited by 18
  2. 2021Implicit field learning for unsupervised anomaly detection in medical imagescited by 17
  3. 2023Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRIcited by 12
  4. 2025NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRIcited by 12
  5. 2022On the Pitfalls of Using the Residual Error as Anomaly Scorecited by 11
  6. 2021Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translationcited by 10
  7. 2024Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETcited by 10
  8. 2024Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detectioncited by 10
  9. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detectioncited by 10
  10. 2023REB: Reducing Biases in Representation for Industrial Anomaly Detectioncited by 9
  11. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  12. 2024IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRIcited by 9
  13. 2022Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 7
  14. 2024Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detectioncited by 7
  15. 2022StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencodercited by 6
  16. 2022What do we learn? Debunking the Myth of Unsupervised Outlier Detectioncited by 6
  17. 2024Towards Universal Unsupervised Anomaly Detection in Medical Imagingcited by 6
  18. 2024Blind Localization and Clustering of Anomalies in Texturescited by 5
  19. 2025REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transportcited by 5
  20. 2025Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixturecited by 5
  21. 2022Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Predictioncited by 4
  22. 2023Bias in Unsupervised Anomaly Detection in Brain MRIcited by 4
  23. 2024Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensemblescited by 4
  24. 2021Anomaly Detection for Solder Joints Using β-VAEcited by 3
  25. 2022Self-Supervised Training with Autoencoders for Visual Anomaly Detectioncited by 3
  26. 2022Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensemblingcited by 3
  27. 2023CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localizationcited by 3
  28. 2023AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detectioncited by 3
  29. 2024Language Models Meet Anomaly Detection for Better Interpretability and Generalizabilitycited by 3
  30. 2024Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasoundcited by 3
  31. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  32. 2025Constitution or Collapse? Exploring Constitutional AI with Llama 3-8Bcited by 3
  33. 2022Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesiscited by 2
  34. 2022Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scanscited by 2
  35. 2024AnoFPDM: Anomaly Segmentation with Forward Process of Diffusion Models for Brain MRIcited by 2
  36. 2025Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysiscited by 2
  37. 2022AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentationcited by 1
  38. 2023Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgerycited by 1

50 most cited of 73 All 73 (JSON)