mlsanityData to 7 Oct 2026

Diffusion Models for Medical Anomaly Detection

arXiv 2203.04306 ↗First posted 8 Mar 2022eess.IV cs.CVcited by 231

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

In medical applications, weakly supervised anomaly detection methods are of great interest, as only image-level annotations are required for training. Current anomaly detection methods mainly rely on generative adversarial networks or autoencoder models. Those models are often complicated to train or have difficulties to preserve fine details in the image. We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models. We combine the deterministic iterative noising and denoising scheme with classifier guidance for image-to-image translation between diseased and healthy subjects. Our method generates very detailed anomaly maps without the need for a complex training procedure. We evaluate our method on the BRATS2020 dataset for brain tumor detection and the CheXpert dataset for detecting pleural effusions.

Key facts. Cited by 231 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 24 of its 31 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

24 of 31 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Label-Efficient Semantic Segmentation with Diffusion Models6 Dec

Other references · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2022Generative Adversarial Networkscited by 5,603
  6. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  7. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  8. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  9. 2019An Introduction to Variational Autoencoderscited by 997
  10. 2021ILVR: Conditioning Method for Denoising Diffusion Probabilistic Modelscited by 634
  11. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  12. 2021UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Modelscited by 133
11 more
  1. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  2. 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
  3. 2018Context-encoding Variational Autoencoder for Unsupervised Anomaly Detectioncited by 63
  4. 2017Visual Feature Attribution using Wasserstein GANscited by 47
  5. 2020Anomaly detection through latent space restoration using vector-quantized variational autoencoderscited by 45
  6. 2019Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localizationcited by 38
  7. 2021Unsupervised Brain Anomaly Detection and Segmentation with Transformerscited by 34
  8. 2021Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRIcited by 23
  9. 2020DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervisioncited by 13
  10. 2021Visual Anomaly Detection for Images: A Surveycited by 11
  11. 2020Assessing the validity of saliency maps for abnormality localization in medical imagingcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 10; 2023: 66; 2024: 78; 2025: 56; 2026: 21.

231 papers.

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  1. 2022Diffusion Models in Vision: A Surveycited by 1,008
  2. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  3. 2022A Survey on Generative Diffusion Modelcited by 238
  4. 2022Unsupervised Medical Image Translation with Adversarial Diffusion Modelscited by 191
  5. 2024AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Errorcited by 123
  6. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  7. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  8. 2023On Diffusion Modeling for Anomaly Detectioncited by 71
  9. 2024Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Modelscited by 69
  10. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  11. 2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
  12. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
38 more
  1. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  2. 2024WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesiscited by 56
  3. 2025Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Modelscited by 54
  4. 2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
  5. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  6. 2024Balancing Act: Distribution-Guided Debiasing in Diffusion Modelscited by 50
  7. 2023Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Modelscited by 48
  8. 2023Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Imagescited by 45
  9. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRIcited by 45
  10. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 38
  11. 2023Don't Play Favorites: Minority Guidance for Diffusion Modelscited by 37
  12. 2024MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detectioncited by 35
  13. 2024Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generationcited by 33
  14. 2023Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesiscited by 32
  15. 2023ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Modelscited by 31
  16. 2023Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processingcited by 30
  17. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Imagescited by 30
  18. 2023Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Modelcited by 29
  19. 2022Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Modelcited by 28
  20. 2023DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasetscited by 28
  21. 2024Diffusion Models with Implicit Guidance for Medical Anomaly Detectioncited by 26
  22. 2023Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Modelcited by 25
  23. 2022The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Modelscited by 24
  24. 2022A Diffusion Model Predicts 3D Shapes from 2D Microscopy Imagescited by 24
  25. 2023Exploring Diffusion Models for Unsupervised Video Anomaly Detectioncited by 23
  26. 2023DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Modelcited by 23
  27. 2024Denoising Diffusion Models for 3D Healthy Brain Tissue Inpaintingcited by 22
  28. 2023Brain Diffuser: An End-to-End Brain Image to Brain Network Pipelinecited by 21
  29. 2023Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognitioncited by 20
  30. 2023Diffusion Models for Contrast Harmonization of Magnetic Resonance Imagescited by 19
  31. 2023Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesiscited by 19
  32. 2023Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representationscited by 18
  33. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIscited by 18
  34. 2024Conditional LoRA Parameter Generationcited by 16
  35. 2023Full-dose Whole-body PET Synthesis from Low-dose PET Using High-efficiency Denoising Diffusion Probabilistic Model: PET Consistency Modelcited by 15
  36. 2025Deep Learning Advancements in Anomaly Detection: A Comprehensive Surveycited by 15
  37. 2023Towards Trustworthy Dataset Distillationcited by 14
  38. 2023DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRIcited by 14

50 most cited of 231 All 231 (JSON)

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  1. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
  2. 2023The role of noise in denoising models for anomaly detection in medical images
  3. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
  4. 2024Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models
  5. 2024Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
  6. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection
  7. 2024Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection
  8. 2023Unsupervised Anomaly Detection using Aggregated Normative Diffusion