# Diffusion Models with Implicit Guidance for Medical Anomaly Detection

arXiv: https://arxiv.org/abs/2403.08464 · HTML: https://mlsanity.com/paper/2403.08464/

## Key facts

- Cited by 26 papers in the map (data through 14 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.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 8 of 8)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Unsupervised Anomaly Localization using Variational Auto-Encoders](https://mlsanity.com/paper/1907.02796/) (2019)
- [Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models](https://mlsanity.com/paper/2305.19643/) (2023)
- [Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI](https://mlsanity.com/paper/2303.03758/) (2023)
- [Detecting Outliers with Poisson Image Interpolation](https://mlsanity.com/paper/2107.02622/) (2021)
- [Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs](https://mlsanity.com/paper/2312.04215/) (2023)

## Built on by (most cited first, 26 of 26)

- [NOVA: A Benchmark for Anomaly Localization and Clinical Reasoning in Brain MRI](https://mlsanity.com/paper/2505.14064/) (2025)
- [MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection](https://mlsanity.com/paper/2502.16943/) (2025)
- [Anomaly Detection and Generation with Diffusion Models: A Survey](https://mlsanity.com/paper/2506.09368/) (2025)
- [Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization](https://mlsanity.com/paper/2506.10233/) (2025)
- [REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport](https://mlsanity.com/paper/2508.02889/) (2025)
- [Physics-Inspired Generative Models in Medical Imaging: A Review](https://mlsanity.com/paper/2407.10856/) (2024)
- [Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes](https://mlsanity.com/paper/2407.15169/) (2024)
- [UltraAD: Fine-Grained Ultrasound Anomaly Classification via Few-Shot CLIP Adaptation](https://mlsanity.com/paper/2506.19694/) (2025)
- [Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles](https://mlsanity.com/paper/2412.17586/) (2024)
- [Denoising Diffusion Models for Anomaly Localization in Medical Images](https://mlsanity.com/paper/2410.23834/) (2024)
- [Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models](https://mlsanity.com/paper/2505.02753/) (2025)
- [OCSVM-Guided Representation Learning for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2507.21164/) (2025)
- [Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review](https://mlsanity.com/paper/2510.14462/) (2025)
- [Provably Reliable Classifier Guidance via Cross-Entropy Control](https://mlsanity.com/paper/2601.21200/) (2026)
- [Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging](https://mlsanity.com/paper/2603.21717/) (2026)
- [Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays](https://mlsanity.com/paper/2505.17311/) (2025)
- [MIAS-SAM: Medical Image Anomaly Segmentation without thresholding](https://mlsanity.com/paper/2505.22762/) (2025)
- [MAMBO: High-Resolution Generative Approach for Mammography Images](https://mlsanity.com/paper/2506.08677/) (2025)
- [CADD: Context aware disease deviations via restoration of brain images using normative conditional diffusion models](https://mlsanity.com/paper/2508.03594/) (2025)
- [AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoring](https://mlsanity.com/paper/2509.12905/) (2025)
- [Generating healthy counterfactuals with denoising diffusion bridge models](https://mlsanity.com/paper/2510.13684/) (2025)
- [BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet](https://mlsanity.com/paper/2511.12853/) (2025)
- [Group Equivariant Diffusion for Anomaly Detection in Computational Cytology](https://mlsanity.com/paper/2607.25503/) (2026)
- [Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection](https://mlsanity.com/paper/2608.00444/) (2026)
- [Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior](https://mlsanity.com/paper/2609.24265/) (2026)
- [MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI](https://mlsanity.com/paper/2610.02136/) (2026)

## 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.