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

TL;DR

This paper introduces a novel conditional diffusion model for brain MRI anomaly detection that uses synthetic pseudo-pathologies generated via fluid-driven randomization, improving detection accuracy without requiring diseased training data.

Contribution

The work presents a weakly supervised framework integrating synthetic pseudo-pathologies into diffusion models for improved anomaly detection in brain MRI, outperforming existing methods.

Findings

Outperforms variational autoencoders and diffusion models in anomaly detection.

Surpasses supervised inpainting methods on most datasets.

Effective in detecting synthetic and real pathologies.

Abstract

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.