mlsanityData to 14 Oct 2026

Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection

arXiv 1812.05941 ↗First posted 14 Dec 2018cs.LG stat.MLcited by 64

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Abstract (arXiv)

Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detecting anomalies in medical images. However, state-of-the-art anomaly scores are still based on the reconstruction error, which lacks in two essential parts: it ignores the model-internal representation employed for reconstruction, and it lacks formal assertions and comparability between samples. We address these shortcomings by proposing the Context-encoding Variational Autoencoder (ceVAE) which combines reconstruction- with density-based anomaly scoring. This improves the sample- as well as pixel-wise results. In our experiments on the BraTS-2017 and ISLES-2015 segmentation benchmarks, the ceVAE achieves unsupervised ROC-AUCs of 0.95 and 0.89, respectively, thus outperforming state-of-the-art methods by a considerable margin.

Key facts. Cited by 64 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2018.

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Citing papers per year: 2021: 14; 2022: 13; 2023: 11; 2024: 14; 2025: 9; 2026: 3.

64 papers.

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  1. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 273
  2. 2022Diffusion Models for Medical Anomaly Detectioncited by 232
  3. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  4. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  5. 2021Consistency Regularization for Variational Auto-Encoderscited by 51
  6. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRIcited by 45
  7. 2021Detecting Outliers with Poisson Image Interpolationcited by 44
  8. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Imagescited by 30
  9. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 26
  10. 2023Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Modelcited by 25
  11. 2023The role of noise in denoising models for anomaly detection in medical imagescited by 23
  12. 2024Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspectivecited by 23
38 more
  1. 2022Constrained unsupervised anomaly segmentationcited by 19
  2. 2021Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Imagescited by 18
  3. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIscited by 18
  4. 2021Implicit field learning for unsupervised anomaly detection in medical imagescited by 17
  5. 2021OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detectioncited by 14
  6. 2023Many tasks make light work: Learning to localise medical anomalies from multiple synthetic taskscited by 12
  7. 2021Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translationcited by 11
  8. 2024CardiacNet: Learning to Reconstruct Abnormalities for Cardiac Disease Assessment from Echocardiogram Videoscited by 11
  9. 2024Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETcited by 10
  10. 2024Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detectioncited by 10
  11. 2023Masked Autoencoders for Unsupervised Anomaly Detection in Medical Imagescited by 9
  12. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  13. 2022StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencodercited by 6
  14. 2022What do we learn? Debunking the Myth of Unsupervised Outlier Detectioncited by 6
  15. 2024MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Imagescited by 6
  16. 2024Towards Universal Unsupervised Anomaly Detection in Medical Imagingcited by 6
  17. 2025Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detectioncited by 6
  18. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomizationcited by 6
  19. 2021Looking at the whole picture: constrained unsupervised anomaly segmentationcited by 4
  20. 2022Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Predictioncited by 4
  21. 2022Assessing Dataset Bias in Computer Visioncited by 4
  22. 2024Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA datacited by 4
  23. 2023CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localizationcited by 3
  24. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  25. 2024Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Imagescited by 3
  26. 2021Leveraging 3D Information in Unsupervised Brain MRI Segmentationcited by 2
  27. 2022Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesiscited by 2
  28. 2022InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registrationcited by 2
  29. 2023Histogram- and Diffusion-Based Medical Out-of-Distribution Detectioncited by 2
  30. 2021Evaluating the Use of Reconstruction Error for Novelty Localizationcited by 1
  31. 2021ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Networkcited by 1
  32. 2024Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Modelscited by 1
  33. 2024SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencodercited by 1
  34. 2025ADMN: A Layer-Wise Adaptive Multimodal Network for Dynamic Input Noise and Compute Resourcescited by 1
  35. 2025Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detectioncited by 1
  36. 2025ART-ASyn: Anatomy-aware Realistic Texture-based Anomaly Synthesis Framework for Chest X-Rayscited by 1
  37. 2021Automated triaging of head MRI examinations using convolutional neural networkscited by 0
  38. 20213-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRIcited by 0

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