Unsupervised Anomaly Detection using Aggregated Normative Diffusion

TL;DR

This paper introduces ANDi, a novel unsupervised anomaly detection method for medical images that outperforms existing approaches in detecting diverse anomalies, especially multiple sclerosis lesions, by leveraging diffusion models.

Contribution

We propose ANDi, a new UAD technique using diffusion models that improves anomaly detection accuracy and robustness across multiple MRI datasets and anomaly types.

Findings

ANDi surpasses recent UAD baselines in multiple datasets.

Up to 178% improvement in AUPRC for MS lesion detection.

Demonstrates increased robustness to various anomaly types.

Abstract

Early detection of anomalies in medical images such as brain MRI is highly relevant for diagnosis and treatment of many conditions. Supervised machine learning methods are limited to a small number of pathologies where there is good availability of labeled data. In contrast, unsupervised anomaly detection (UAD) has the potential to identify a broader spectrum of anomalies by spotting deviations from normal patterns. Our research demonstrates that existing state-of-the-art UAD approaches do not generalise well to diverse types of anomalies in realistic multi-modal MR data. To overcome this, we introduce a new UAD method named Aggregated Normative Diffusion (ANDi). ANDi operates by aggregating differences between predicted denoising steps and ground truth backwards transitions in Denoising Diffusion Probabilistic Models (DDPMs) that have been trained on pyramidal Gaussian noise. We validate ANDi against three recent UAD baselines, and across three diverse brain MRI datasets. We show that ANDi, in some cases, substantially surpasses these baselines and shows increased robustness to varying types of anomalies. Particularly in detecting multiple sclerosis (MS) lesions, ANDi achieves improvements of up to 178% in terms of AUPRC.