# Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization

arXiv: https://arxiv.org/abs/2506.10233 · HTML: https://mlsanity.com/paper/2506.10233/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 30 of its 46 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Placed in

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

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Diffusion Autoencoders: Toward a Meaningful and Decodable Representation](https://mlsanity.com/paper/2111.15640/) (2021)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study](https://mlsanity.com/paper/2004.03271/) (2020)
- [SynthMorph: learning contrast-invariant registration without acquired images](https://mlsanity.com/paper/2004.10282/) (2020)
- [Denoising diffusion models for out-of-distribution detection](https://mlsanity.com/paper/2211.07740/) (2022)
- [Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models](https://mlsanity.com/paper/2206.03461/) (2022)
- [Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders](https://mlsanity.com/paper/1806.04972/) (2018)
- [Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance](https://mlsanity.com/paper/1812.02765/) (2018)
- [Unsupervised Lesion Detection via Image Restoration with a Normative Prior](https://mlsanity.com/paper/2005.00031/) (2020)
- [Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1812.05941/) (2018)
- [Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection](https://mlsanity.com/paper/2203.02194/) (2022)
- [Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images](https://mlsanity.com/paper/2008.03632/) (2020)
- [Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models](https://mlsanity.com/paper/2305.19643/) (2023)
- [Unsupervised Brain Anomaly Detection and Segmentation with Transformers](https://mlsanity.com/paper/2102.11650/) (2021)
- [Diffusion Models with Implicit Guidance for Medical Anomaly Detection](https://mlsanity.com/paper/2403.08464/) (2024)
- [Unsupervised 3D out-of-distribution detection with latent diffusion models](https://mlsanity.com/paper/2307.03777/) (2023)
- [Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting](https://mlsanity.com/paper/2403.14499/) (2024)
- [Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI](https://mlsanity.com/paper/2312.05119/) (2023)
- [Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging](https://mlsanity.com/paper/2311.16914/) (2023)
- [Multi-scale Cross-restoration Framework for Electrocardiogram Anomaly Detection](https://mlsanity.com/paper/2308.01639/) (2023)
- [PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI](https://mlsanity.com/paper/2403.06227/) (2024)
- [What do we learn? Debunking the Myth of Unsupervised Outlier Detection](https://mlsanity.com/paper/2206.03698/) (2022)
- [Unraveling Normal Anatomy via Fluid-Driven Anomaly Randomization](https://mlsanity.com/paper/2501.13370/) (2025)

## Built on by (most cited first, 6 of 6)

- [USB: Unified Synthetic Brain Framework for Bidirectional Pathology-Healthy Generation and Editing](https://mlsanity.com/paper/2512.00269/) (2025)
- [Treatment-Conditioned Diffusion for Forecasting Neurodegenerative Disease Progression](https://mlsanity.com/paper/2605.29932/) (2026)
- [Enhancing Corpus Callosum Segmentation in Fetal MRI via Pathology-Informed Domain Randomization](https://mlsanity.com/paper/2508.20475/) (2025)
- [Generating healthy counterfactuals with denoising diffusion bridge models](https://mlsanity.com/paper/2510.13684/) (2025)
- [Deep generative priors for 3D brain analysis](https://mlsanity.com/paper/2510.15119/) (2025)
- [Rethinking Structural Anomaly Detection: From Decision Boundaries to Projection Operators](https://mlsanity.com/paper/2606.15280/) (2026)

## Abstract (arXiv)

Supervised machine learning has enabled accurate pathology detection in brain MRI, but requires training data from diseased subjects that may not be readily available in some scenarios, for example, in the case of rare diseases. Reconstruction-based unsupervised anomaly detection, in particular using diffusion models, has gained popularity in the medical field as it allows for training on healthy images alone, eliminating the need for large disease-specific cohorts. These methods assume that a model trained on normal data cannot accurately represent or reconstruct anomalies. However, this assumption often fails with models failing to reconstruct healthy tissue or accurately reconstruct abnormal regions i.e., failing to remove anomalies. In this work, we introduce a novel conditional diffusion model framework for anomaly detection and healthy image reconstruction in brain MRI. Our weakly supervised approach integrates synthetically generated pseudo-pathology images into the modeling process to better guide the reconstruction of healthy images. To generate these pseudo-pathologies, we apply fluid-driven anomaly randomization to augment real pathology segmentation maps from an auxiliary dataset, ensuring that the synthetic anomalies are both realistic and anatomically coherent. We evaluate our model's ability to detect pathology, using both synthetic anomaly datasets and real pathology from the ATLAS dataset. In our extensive experiments, our model: (i) consistently outperforms variational autoencoders, and conditional and unconditional latent diffusion; and (ii) surpasses on most datasets, the performance of supervised inpainting methods with access to paired diseased/healthy images.