mlsanityData to 13 Oct 2026

Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model

arXiv 2305.19867 ↗First posted 31 May 2023eess.IV cs.CVcited by 25

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

It can be challenging to identify brain MRI anomalies using supervised deep-learning techniques due to anatomical heterogeneity and the requirement for pixel-level labeling. Unsupervised anomaly detection approaches provide an alternative solution by relying only on sample-level labels of healthy brains to generate a desired representation to identify abnormalities at the pixel level. Although, generative models are crucial for generating such anatomically consistent representations of healthy brains, accurately generating the intricate anatomy of the human brain remains a challenge. In this study, we present a method called masked-DDPM (mDPPM), which introduces masking-based regularization to reframe the generation task of diffusion models. Specifically, we introduce Masked Image Modeling (MIM) and Masked Frequency Modeling (MFM) in our self-supervised approach that enables models to learn visual representations from unlabeled data. To the best of our knowledge, this is the first attempt to apply MFM in DPPM models for medical applications. We evaluate our approach on datasets containing tumors and numerous sclerosis lesions and exhibit the superior performance of our unsupervised method as compared to the existing fully/weakly supervised baselines. Code is available at https://github.com/hasan1292/mDDPM.

Key facts. Cited by 25 papers in the map (data through 13 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 20 of its 35 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

20 of 35 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI7 Mar
  2. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models7 Jun
  3. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 17

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,777
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,364
  4. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,425
  5. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 514
  6. 2017Deep Learning for Medical Image Analysiscited by 400
  7. 2022Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Modelscited by 215
  8. 2019Deep learning with noisy labels: exploring techniques and remedies in medical image analysiscited by 173
  9. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 147
  10. 2019Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolutioncited by 140
  11. 2020Deep Learning for Medical Anomaly Detection -- A Surveycited by 127
  12. 2022ConvMAE: Masked Convolution Meets Masked Autoencoderscited by 105
5 more
  1. 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
  2. 2018Context-encoding Variational Autoencoder for Unsupervised Anomaly Detectioncited by 64
  3. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  4. 2022Constrained unsupervised anomaly segmentationcited by 19
  5. 2020Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpaintingcited by 15

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 2; 2024: 10; 2025: 10; 2026: 3.

25 papers.

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  1. 2024Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generationcited by 33
  2. 2023LatentEditor: Text Driven Local Editing of 3D Scenescited by 17
  3. 2025A Survey on Diffusion Models for Anomaly Detectioncited by 13
  4. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detectioncited by 10
  5. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  6. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  7. 2024Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detectioncited by 7
  8. 2024Vision-Language Synthetic Data Enhances Echocardiography Downstream Taskscited by 6
  9. 2025Applications of Large Models in Medicinecited by 6
  10. 2024Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensemblescited by 4
  11. 2025RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentationcited by 3
  12. 2024AnoFPDM: Anomaly Segmentation with Forward Process of Diffusion Models for Brain MRIcited by 2
13 more
  1. 2024MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRIcited by 2
  2. 2024Statistical Test for Diffusion-Based Anomaly Localization via Selective Inferencecited by 1
  3. 2024Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion modelscited by 1
  4. 2024Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosiscited by 1
  5. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1
  6. 2024Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspectivecited by 0
  7. 2025Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rayscited by 0
  8. 2025CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion modelscited by 0
  9. 2025RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scanscited by 0
  10. 2025FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRIcited by 0
  11. 2026MedAD-R1: Eliciting Consistent Reasoning in Interpretible Medical Anomaly Detection via Consistency-Reinforced Policy Optimizationcited by 0
  12. 2026PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detectioncited by 0
  13. 2026Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images
  2. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
  3. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
  4. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images
  5. 2023Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI
  6. 2024Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI
  7. 2026Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior
  8. 2023The role of noise in denoising models for anomaly detection in medical images