mlsanityData to 7 Oct 2026

Diffusion Models with Implicit Guidance for Medical Anomaly Detection

arXiv 2403.08464 ↗First posted 13 Mar 2024eess.IV cs.CV cs.LGcited by 26

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents. Nonetheless, standard approaches may compromise critical information during pathology removal, leading to restorations that do not align with unaffected regions in the original scans. Such discrepancies can inadvertently increase false positive rates and reduce specificity, complicating radiological evaluations. This paper introduces Temporal Harmonization for Optimal Restoration (THOR), which refines the de-noising process by integrating implicit guidance through temporal anomaly maps. THOR aims to preserve the integrity of healthy tissue in areas unaffected by pathology. Comparative evaluations show that THOR surpasses existing diffusion-based methods in detecting and segmenting anomalies in brain MRIs and wrist X-rays. Code: https://github.com/ci-ber/THOR_DDPM.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 8 of its 15 references that are in the map. First posted on arXiv 13 Mar 2024.

Builds on

8 of 15 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs7 Dec
  2. 2023Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models31 May
  3. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI7 Mar
  4. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 4

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2019Unsupervised Anomaly Localization using Variational Auto-Encoderscited by 73
  4. 2021Detecting Outliers with Poisson Image Interpolationcited by 44

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 16; 2026: 6.

26 papers.

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  1. 2025NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRIcited by 12
  2. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detectioncited by 10
  3. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  4. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomizationcited by 6
  5. 2024Physics-Inspired Generative Models in Medical Imaging: A Reviewcited by 5
  6. 2024Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakescited by 5
  7. 2025UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptationcited by 5
  8. 2025REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transportcited by 5
  9. 2024Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensemblescited by 4
  10. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  11. 2025Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Modelscited by 1
  12. 2025OCSVM-Guided Representation Learning for Unsupervised Anomaly Detectioncited by 1
14 more
  1. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1
  2. 2026Provably Reliable Classifier Guidance via Cross-Entropy Controlcited by 1
  3. 2026Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imagingcited by 1
  4. 2025Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rayscited by 0
  5. 2025MIAS-SAM: Medical Image Anomaly Segmentation without thresholdingcited by 0
  6. 2025MAMBO: High-Resolution Generative Approach for Mammography Imagescited by 0
  7. 2025CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion modelscited by 0
  8. 2025AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoringcited by 0
  9. 2025Generating healthy counterfactuals with denoising diffusion bridge modelscited by 0
  10. 2025BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNetcited by 0
  11. 2026Group Equivariant Diffusion for Anomaly Detection in Computational Cytologycited by 0
  12. 2026Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detectioncited by 0
  13. 2026Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Priorcited by 0
  14. 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. 2024Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
  2. 2023DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
  3. 2023Unsupervised Anomaly Detection using Aggregated Normative Diffusion
  4. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images
  5. 2026Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions
  6. 2024Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
  7. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models
  8. 2024MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications