mlsanityData to 7 Oct 2026

Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders

arXiv 1806.04972 ↗First posted 13 Jun 2018cs.CVcited by 83

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Abstract (arXiv)

Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task. State-of-the-art approaches are mostly based on supervised learning making use of large annotated datasets. Human beings, on the other hand, even non-experts, can detect most abnormal lesions after seeing a handful of healthy brain images. Replicating this capability of using prior information on the appearance of healthy brain structure to detect lesions can help computers achieve human level abnormality detection, specifically reducing the need for numerous labeled examples and bettering generalization of previously unseen lesions. To this end, we study detection of lesion regions in an unsupervised manner by learning data distribution of brain MRI of healthy subjects using auto-encoder based methods. We hypothesize that one of the main limitations of the current models is the lack of consistency in latent representation. We propose a simple yet effective constraint that helps mapping of an image bearing lesion close to its corresponding healthy image in the latent space. We use the Human Connectome Project dataset to learn distribution of healthy-appearing brain MRI and report improved detection, in terms of AUC, of the lesions in the BRATS challenge dataset.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jun 2018.

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Citing papers per year: 2021: 20; 2022: 15; 2023: 20; 2024: 15; 2025: 11; 2026: 2.

83 papers.

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  1. 2022Diffusion Models for Medical Anomaly Detectioncited by 231
  2. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 98
  3. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  4. 2022Latent Outlier Exposure for Anomaly Detection with Contaminated Datacited by 48
  5. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  6. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRIcited by 45
  7. 2021Detecting Outliers with Poisson Image Interpolationcited by 44
  8. 2021SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detectioncited by 34
  9. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Imagescited by 30
  10. 2023Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Modelcited by 25
  11. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  12. 2023Unsupervised Pathology Detection: A Deep Dive Into the State of the Artcited by 24
38 more
  1. 2023The role of noise in denoising models for anomaly detection in medical imagescited by 23
  2. 2024Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspectivecited by 22
  3. 2022Constrained unsupervised anomaly segmentationcited by 19
  4. 2021Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Imagescited by 18
  5. 2023Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detectioncited by 18
  6. 2021EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognitioncited by 15
  7. 2021ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentationcited by 15
  8. 2023Beyond Individual Input for Deep Anomaly Detection on Tabular Datacited by 14
  9. 2024Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Imagescited by 13
  10. 2023Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videoscited by 12
  11. 2022On the Pitfalls of Using the Residual Error as Anomaly Scorecited by 11
  12. 2021Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translationcited by 10
  13. 2024Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETcited by 10
  14. 2023Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Imagescited by 9
  15. 2023Masked Autoencoders for Unsupervised Anomaly Detection in Medical Imagescited by 9
  16. 2021A multi-stage GAN for multi-organ chest X-ray image generation and segmentationcited by 8
  17. 2023Model Selection of Anomaly Detectors in the Absence of Labeled Validation Datacited by 8
  18. 2022Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 7
  19. 2023Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detectioncited by 7
  20. 2022HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Diseasecited by 6
  21. 2024Retrieval Augmented Deep Anomaly Detection for Tabular Datacited by 6
  22. 2024Statistical Test for Anomaly Detections by Variational Auto-Encoderscited by 6
  23. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomizationcited by 6
  24. 2022Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Modelscited by 5
  25. 2025Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixturecited by 5
  26. 2021Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Networkcited by 4
  27. 2021Looking at the whole picture: constrained unsupervised anomaly segmentationcited by 4
  28. 2021Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detectioncited by 4
  29. 2023Unsupervised Anomaly Detection using Aggregated Normative Diffusioncited by 4
  30. 2024Disentangling Tabular Data Towards Better One-Class Anomaly Detectioncited by 4
  31. 2022An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial trainingcited by 3
  32. 2022Self-Supervised Training with Autoencoders for Visual Anomaly Detectioncited by 3
  33. 2023A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detectioncited by 3
  34. 2021Leveraging 3D Information in Unsupervised Brain MRI Segmentationcited by 2
  35. 2021Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentationcited by 2
  36. 2021Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal imagecited by 2
  37. 2022Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesiscited by 2
  38. 2022ManiFlow: Implicitly Representing Manifolds with Normalizing Flowscited by 2

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