mlsanityData to 7 Oct 2026

Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models

arXiv 2305.19643 ↗First posted 31 May 2023cs.CV cs.AI eess.IVcited by 48

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

The introduction of diffusion models in anomaly detection has paved the way for more effective and accurate image reconstruction in pathologies. However, the current limitations in controlling noise granularity hinder diffusion models' ability to generalize across diverse anomaly types and compromise the restoration of healthy tissues. To overcome these challenges, we propose AutoDDPM, a novel approach that enhances the robustness of diffusion models. AutoDDPM utilizes diffusion models to generate initial likelihood maps of potential anomalies and seamlessly integrates them with the original image. Through joint noised distribution re-sampling, AutoDDPM achieves harmonization and in-painting effects. Our study demonstrates the efficacy of AutoDDPM in replacing anomalous regions while preserving healthy tissues, considerably surpassing diffusion models' limitations. It also contributes valuable insights and analysis on the limitations of current diffusion models, promoting robust and interpretable anomaly detection in medical imaging - an essential aspect of building autonomous clinical decision systems with higher interpretability.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 16 of its 24 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

16 of 24 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI7 Mar
  2. 2022Denoising diffusion models for out-of-distribution detection14 Nov
  3. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  5. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  6. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  7. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  8. 2019Unsupervised Anomaly Localization using Variational Auto-Encoderscited by 73
  9. 2020Unsupervised Lesion Detection via Image Restoration with a Normative Priorcited by 65
  10. 2021fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Datacited by 29
  11. 2021Autoencoding Under Normalization Constraintscited by 26
  12. 2023Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detectioncited by 18
1 more
  1. 2022What do we learn? Debunking the Myth of Unsupervised Outlier Detectioncited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 10; 2024: 18; 2025: 15; 2026: 5.

48 papers.

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  1. 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 59
  2. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Imagescited by 30
  3. 2024Diffusion Models with Implicit Guidance for Medical Anomaly Detectioncited by 26
  4. 2023DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Modelcited by 23
  5. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIscited by 18
  6. 2023DreaMR: Diffusion-driven Counterfactual Explanation for Functional MRIcited by 14
  7. 2025A Survey on Diffusion Models for Anomaly Detectioncited by 13
  8. 2023Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRIcited by 12
  9. 2024Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETcited by 10
  10. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detectioncited by 10
  11. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  12. 2024IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRIcited by 9
36 more
  1. 2024Diff-Def: Diffusion-Generated Deformation Fields for Conditional Atlasescited by 8
  2. 2024MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRIcited by 8
  3. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  4. 2024Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detectioncited by 7
  5. 2024Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesiscited by 7
  6. 2024MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applicationscited by 7
  7. 2023Probing the Limits and Capabilities of Diffusion Models for the Anatomic Editing of Digital Twinscited by 6
  8. 2024Towards Universal Unsupervised Anomaly Detection in Medical Imagingcited by 6
  9. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomizationcited by 6
  10. 2024Unsupervised Anomaly Detection via Masked Diffusion Posterior Samplingcited by 5
  11. 2025REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transportcited by 5
  12. 2023Unsupervised Anomaly Detection using Aggregated Normative Diffusioncited by 4
  13. 2024Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensemblescited by 4
  14. 2024Language Models Meet Anomaly Detection for Better Interpretability and Generalizabilitycited by 3
  15. 2024Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasoundcited by 3
  16. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  17. 2024Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Imagescited by 3
  18. 2025Constitution or Collapse? Exploring Constitutional AI with Llama 3-8Bcited by 3
  19. 2023Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategycited by 2
  20. 2024MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRIcited by 2
  21. 2025Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysiscited by 2
  22. 2025Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detectioncited by 1
  23. 2025OCSVM-Guided Representation Learning for Unsupervised Anomaly Detectioncited by 1
  24. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1
  25. 2025ART-ASyn: Anatomy-aware Realistic Texture-based Anomaly Synthesis Framework for Chest X-Rayscited by 1
  26. 2024Objective and Interpretable Breast Cosmesis Evaluation with Attention Guided Denoising Diffusion Anomaly Detection Modelcited by 0
  27. 2024Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detectioncited by 0
  28. 2025CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion modelscited by 0
  29. 2025AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoringcited by 0
  30. 2025Generating healthy counterfactuals with denoising diffusion bridge modelscited by 0
  31. 2025Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learningcited by 0
  32. 2026Semantic Iterative Reconstruction: One-Shot Universal Anomaly Detectioncited by 0
  33. 2026Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approachcited by 0
  34. 2026Group Equivariant Diffusion for Anomaly Detection in Computational Cytologycited by 0
  35. 2026Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Priorcited by 0
  36. 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. 2026Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection
  2. 2024MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images
  3. 2023DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
  4. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
  5. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey
  6. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
  7. 2025Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection
  8. 2024GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection