mlsanityData to 14 Oct 2026

BMAD: Benchmarks for Medical Anomaly Detection

arXiv 2306.11876 ↗First posted 20 Jun 2023eess.IV cs.CVcited by 82

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Abstract (arXiv)

Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To bridge this gap, we introduce a comprehensive evaluation benchmark for assessing anomaly detection methods on medical images. This benchmark encompasses six reorganized datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT, chest X-ray, and digital histopathology) and three key evaluation metrics, and includes a total of fourteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables comprehensive comparisons among recently proposed anomaly detection methods. It will facilitate the community to conduct a fair comparison and advance the field of AD on medical imaging. More information on BMAD is available in our GitHub repository: https://github.com/DorisBao/BMAD

Key facts. Cited by 82 papers in the map (data through 14 Oct 2026). Builds on 49 of its 81 references that are in the map. First posted on arXiv 20 Jun 2023.

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49 of 81 references.

References · 49

  1. 2015Variational Inference with Normalizing Flowscited by 2,170
  2. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  3. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  4. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  5. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  6. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  7. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  8. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  9. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  10. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 509
  11. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  12. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
37 more
  1. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  2. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  3. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  4. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  5. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 268
  6. 2018Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoderscited by 264
  7. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  8. 2022Diffusion Models for Medical Anomaly Detectioncited by 232
  9. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  10. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 227
  11. 2020Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentationcited by 212
  12. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
  13. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  14. 2019OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representationscited by 199
  15. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  16. 2021Fully Convolutional Cross-Scale-Flows for Image-based Defect Detectioncited by 117
  17. 2022Asymmetric Student-Teacher Networks for Industrial Anomaly Detectioncited by 117
  18. 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
  19. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  20. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  21. 2023IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturingcited by 64
  22. 2020Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Imagescited by 52
  23. 2020Detecting Outliers with Foreign Patch Interpolationcited by 44
  24. 2021Unsupervised Brain Anomaly Detection and Segmentation with Transformerscited by 34
  25. 2022ADTR: Anomaly Detection Transformer with Feature Reconstructioncited by 31
  26. 2022AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decodercited by 29
  27. 2021Reconstruction Student with Attention for Student-Teacher Pyramid Matchingcited by 20
  28. 2021Anomaly Detection via Self-organizing Mapcited by 18
  29. 2018Cancer Metastasis Detection With Neural Conditional Random Fieldcited by 17
  30. 2021ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentationcited by 15
  31. 2019Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Imagecited by 14
  32. 2022Benchmarking Unsupervised Anomaly Detection and Localizationcited by 11
  33. 2022Unsupervised Visual Defect Detection with Score-Based Generative Modelcited by 7
  34. 2019Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencodercited by 4
  35. 2019Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Imagescited by 4
  36. 2023ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and Localizationcited by 4
  37. 2023Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Datacited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 12; 2025: 38; 2026: 29.

82 papers.

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  1. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  2. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  3. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  4. 2023Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detectioncited by 47
  5. 2023DiAD: A Diffusion-based Framework for Multi-class Anomaly Detectioncited by 33
  6. 2024UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detectioncited by 28
  7. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 26
  8. 20253D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Taskscited by 26
  9. 2024IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengerscited by 20
  10. 2024PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edgecited by 16
  11. 2024Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learningcited by 13
  12. 2025Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoringcited by 10
38 more
  1. 2025FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localizationcited by 8
  2. 2025Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentationcited by 8
  3. 2025AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detectioncited by 8
  4. 2024MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applicationscited by 7
  5. 2025Applications of Large Models in Medicinecited by 6
  6. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  7. 2025Is Hyperbolic Space All You Need for Medical Anomaly Detection?cited by 5
  8. 2025Anomaly Detection by Clustering DINO Embeddings using a Dirichlet Process Mixturecited by 5
  9. 2024Bidirectional Multimodal Prompt Learning with Scale-Aware Training for Few-Shot Multi-Class Anomaly Detectioncited by 4
  10. 2025Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detectioncited by 4
  11. 2025We Need to Rethink Benchmarking in Anomaly Detectioncited by 4
  12. 2025No Label Left Behind: A Unified Surface Defect Detection Model for all Supervision Regimescited by 4
  13. 2025InvAD: Inversion-based Reconstruction-Free Anomaly Detection with Diffusion Modelscited by 3
  14. 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholdscited by 3
  15. 2025Towards Continual Visual Anomaly Detection in the Medical Domaincited by 3
  16. 2024Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detectioncited by 2
  17. 2024FlowCLAS: Enhancing Normalizing Flow Via Contrastive Learning For Anomaly Segmentationcited by 2
  18. 2025MADPOT: Medical Anomaly Detection with CLIP Adaptation and Partial Optimal Transportcited by 2
  19. 2025Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detectioncited by 2
  20. 2026DINO-AD: Unsupervised Anomaly Detection with Frozen DINO-V3 Featurescited by 2
  21. 2023AnoDODE: Anomaly Detection with Diffusion ODEcited by 1
  22. 2025IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domaincited by 1
  23. 2025Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathologycited by 1
  24. 2025BenchReAD: A systematic benchmark for retinal anomaly detectioncited by 1
  25. 2025Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Conceptscited by 1
  26. 2025A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detectioncited by 1
  27. 2026Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutionscited by 1
  28. 2026CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detectioncited by 1
  29. 2024LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Spacecited by 0
  30. 2025When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcitycited by 0
  31. 2025Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) Algorithmscited by 0
  32. 2025SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Imagescited by 0
  33. 2025Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rayscited by 0
  34. 2025PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddingscited by 0
  35. 2025MIAS-SAM: Medical Image Anomaly Segmentation without thresholdingcited by 0
  36. 2025MoViAD: A Modular Library for Visual Anomaly Detectioncited by 0
  37. 2025SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detectioncited by 0
  38. 2025Detection of Medial Epicondyle Avulsion in Elbow Ultrasound Images via Bone Structure Reconstructioncited by 0

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