mlsanityData to 7 Oct 2026

Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI

arXiv 2303.03758 ↗First posted 7 Mar 2023eess.IV cs.CVcited by 45

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

The use of supervised deep learning techniques to detect pathologies in brain MRI scans can be challenging due to the diversity of brain anatomy and the need for annotated data sets. An alternative approach is to use unsupervised anomaly detection, which only requires sample-level labels of healthy brains to create a reference representation. This reference representation can then be compared to unhealthy brain anatomy in a pixel-wise manner to identify abnormalities. To accomplish this, generative models are needed to create anatomically consistent MRI scans of healthy brains. While recent diffusion models have shown promise in this task, accurately generating the complex structure of the human brain remains a challenge. In this paper, we propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy, using spatial context to guide and improve reconstruction. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 25 of its 41 references that are in the map. First posted on arXiv 7 Mar 2023.

Builds on

25 of 41 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Denoising diffusion models for out-of-distribution detection14 Nov
  2. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models7 Jun
  3. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 22

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
  6. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  7. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 935
  8. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  9. 2017Deep Learning for Medical Image Analysiscited by 399
  10. 2022Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Modelscited by 215
  11. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  12. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
10 more
  1. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 146
  2. 2019Automated brain extraction of multi-sequence MRI using artificial neural networkscited by 133
  3. 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
  4. 2020Unsupervised Lesion Detection via Image Restoration with a Normative Priorcited by 65
  5. 2018Context-encoding Variational Autoencoder for Unsupervised Anomaly Detectioncited by 63
  6. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  7. 2021Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRIcited by 23
  8. 2020Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRIcited by 22
  9. 2022Constrained unsupervised anomaly segmentationcited by 19
  10. 2020Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpaintingcited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 21; 2025: 10; 2026: 7.

45 papers.

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  1. 2023Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Modelscited by 48
  2. 2023Patched Denoising Diffusion Models For High-Resolution Image Synthesiscited by 45
  3. 2024Diffusion Models with Implicit Guidance for Medical Anomaly Detectioncited by 26
  4. 2023Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Modelcited by 25
  5. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  6. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIscited by 18
  7. 2024Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detectioncited by 10
  8. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detectioncited by 10
  9. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  10. 2024Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detectioncited by 7
  11. 2024MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Imagescited by 6
  12. 2024Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detectioncited by 5
33 more
  1. 2025REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transportcited by 5
  2. 2024Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensemblescited by 4
  3. 2023Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Modelscited by 3
  4. 2024Language Models Meet Anomaly Detection for Better Interpretability and Generalizabilitycited by 3
  5. 2024Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasoundcited by 3
  6. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  7. 2024Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Imagescited by 3
  8. 2023Histogram- and Diffusion-Based Medical Out-of-Distribution Detectioncited by 2
  9. 2024AnoFPDM: Anomaly Segmentation with Forward Process of Diffusion Models for Brain MRIcited by 2
  10. 2024MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRIcited by 2
  11. 2025Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstructioncited by 2
  12. 2024Discrepancy-based Diffusion Models for Lesion Detection in Brain MRIcited by 1
  13. 2024Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion modelscited by 1
  14. 2024Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Modelscited by 1
  15. 2024Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosiscited by 1
  16. 2024Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representationscited by 1
  17. 2025Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detectioncited by 1
  18. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1
  19. 2024Tau Anomaly Detection in PET Imaging via Bilateral-Guided Deterministic Diffusion Modelcited by 0
  20. 2024Localizing Anomalies via Multiscale Score Matching Analysiscited by 0
  21. 2024Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspectivecited by 0
  22. 2025Screener: Self-supervised Pathology Segmentation in Medical CT Imagescited by 0
  23. 2025U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cordcited by 0
  24. 2025Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstructioncited by 0
  25. 2025RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scanscited by 0
  26. 2025FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRIcited by 0
  27. 2026Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imagingcited by 0
  28. 2026MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectorscited by 0
  29. 2026Normal Guidance is what Attention Needscited by 0
  30. 2026Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Modelscited by 0
  31. 2026Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detectioncited by 0
  32. 2026Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Priorcited by 0
  33. 2026MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRIcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
  2. 2025Deep generative priors for 3D brain analysis
  3. 2025CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion models
  4. 2023DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
  5. 2023Unsupervised Anomaly Detection using Aggregated Normative Diffusion
  6. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images
  7. 2024Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
  8. 2025Integrating Anatomical Priors into a Causal Diffusion Model