mlsanityData to 14 Oct 2026

Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection

arXiv 2608.00444 ↗First posted 1 Aug 2026cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Unsupervised medical anomaly detection learns normal anatomical patterns from healthy training images and identifies deviations at test time. Reconstruction-based and diffusion-based methods commonly use the difference between an input image and its reconstruction as anomaly evidence. However, this residual can be ambiguous. Expressive models may preserve pathological structures, while benign anatomical variation, imaging noise, and acquisition differences may also produce large reconstruction errors. We propose discriminative mask-guided diffusion (DMD), a medical anomaly detection framework that complements residual-based localization with reconstruction-shift discrimination. DMD first learns a compact quantized latent representation of normal images. Localized masks then perturb selected latent regions, and a latent diffusion model reconstructs the perturbed representations. The resulting reconstructions are paired with their original normal images to define a self-supervised classification task. At inference, the classifier provides a learned image-level anomaly score, while the residual between the input and its diffusion-based reconstruction yields a pixel-level anomaly map. Experiments on five datasets spanning brain MRI, breast ultrasound, and chest radiography show that DMD achieves the best overall performance among the state-of-the-art baseline methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 18 of its 32 references that are in the map. First posted on arXiv 1 Aug 2026.

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18 of 32 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection24 Feb
  2. 2024Diffusion Models with Implicit Guidance for Medical Anomaly Detection13 Mar
  3. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs7 Dec
  4. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images3 Aug
  5. 2023Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Model31 May
  6. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI7 Mar
  7. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models7 Jun
  8. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 10

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 531
  3. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  4. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  5. 2015Adversarial Autoencoderscited by 364
  6. 2020VinDr-CXR: An open dataset of chest X-rays with radiologist's annotationscited by 239
  7. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 147
  8. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  9. 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
  10. 2018Unsupervised brain lesion segmentation from MRI using a convolutional autoencodercited by 17

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  1. 2024Denoising Diffusion Models for Anomaly Localization in Medical Images
  2. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
  3. 2023Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models
  4. 2026Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior
  5. 2024Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
  6. 2024MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images
  7. 2023DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
  8. 2025CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion models