mlsanityData to 7 Oct 2026

Denoising Diffusion Models for Anomaly Localization in Medical Images

arXiv 2410.23834 ↗First posted 31 Oct 2024eess.IV cs.CVcited by 3

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

This review explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and their conditioning using guidance mechanisms, we provide an overview of available datasets and evaluation metrics suitable for their application to anomaly localization in medical images. In this context, we discuss supervision schemes ranging from fully supervised segmentation to semi-supervised, weakly supervised, self-supervised, and unsupervised methods, and provide insights into the effectiveness and limitations of these approaches. Furthermore, we highlight open challenges in anomaly localization, including detection bias, domain shift, computational cost, and model interpretability. Our goal is to provide an overview of the current state of the art in the field, outline research gaps, and highlight the potential of diffusion models for robust anomaly localization in medical images.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 91 of its 134 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

91 of 134 references.

Closest ancestors · 35

Same direction, newest first

  1. 2024AnoFPDM: Anomaly Segmentation with Forward Process of Diffusion Models for Brain MRI24 Apr
  2. 2024Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting21 Mar
  3. 2024Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection21 Mar
  4. 2024Diffusion Models with Implicit Guidance for Medical Anomaly Detection13 Mar
  5. 2024WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis29 Feb
  6. 2023Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation19 Dec
  7. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs7 Dec
  8. 2023DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection26 Nov
  9. 2023Robust semi-supervised segmentation with timestep ensembling diffusion models13 Nov
  10. 2023DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model19 Oct
  11. 2023Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI30 Aug
  12. 2023DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation5 Aug
23 more
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  2. 2023Pre-Training with Diffusion models for Dental Radiography segmentation26 Jul
  3. 2023Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis19 Jul
  4. 2023Unsupervised 3D out-of-distribution detection with latent diffusion models7 Jul
  5. 2023DifFSS: Diffusion Model for Few-Shot Semantic Segmentation3 Jul
  6. 2023Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models15 Jun
  7. 2023Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation6 Jun
  8. 2023Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models31 May
  9. 2023Ambiguous Medical Image Segmentation using Diffusion Models10 Apr
  10. 2023BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation10 Apr
  11. 2023Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing27 Mar
  12. 2023Stochastic Segmentation with Conditional Categorical Diffusion Models15 Mar
  13. 2023Diffusion Models for Contrast Harmonization of Magnetic Resonance Images14 Mar
  14. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI7 Mar
  15. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer19 Jan
  16. 2022MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model1 Nov
  17. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep
  18. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models7 Jun
  19. 2022The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Models6 Apr
  20. 2022Diffusion Models for Medical Anomaly Detection8 Mar
  21. 2021Diffusion Models for Implicit Image Segmentation Ensembles6 Dec
  22. 2021Label-Efficient Semantic Segmentation with Diffusion Models6 Dec
  23. 2021SegDiff: Image Segmentation with Diffusion Probabilistic Models1 Dec

Other references · 56

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  7. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
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  9. 2022Classifier-Free Diffusion Guidancecited by 5,717
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  11. 2022Flow Matching for Generative Modelingcited by 5,426
  12. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
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  3. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  4. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
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  6. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
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  15. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
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  21. 2022Fast Sampling of Diffusion Models via Operator Learningcited by 173
  22. 2023Denoising Diffusion Autoencoders are Unified Self-supervised Learnerscited by 128
  23. 2023On Memorization in Diffusion Modelscited by 122
  24. 2019Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challengecited by 115
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  32. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  33. 2021Detecting Outliers with Poisson Image Interpolationcited by 44
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50 most cited of 56 All 56 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1
  2. 2025MIAS-SAM: Medical Image Anomaly Segmentation without thresholdingcited by 0
  3. 2026Group Equivariant Diffusion for Anomaly Detection in Computational Cytologycited by 0

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  8. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection