Work

Unsupervised anomaly detection in medical imaging using aggregated normative diffusion

Year: 2025

Type: article

Abstract: Early detection of anomalies in medical images such as brain magnetic resonance imaging (MRI) is highly relevant for diagnosis and treatment of many medical 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 previous state-of-the-art UAD approaches do not generalise well to diverse types of anomalies in multi-modal MRI 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 four 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 44 % (0.302 to 0.436 on Lubljana, +0.134) in terms of AUPRC.

Source: Medical Image Analysis

DOI: 10.1016/j.media.2025.103895

Authors: Alexander Frotscher, Jaivardhan Kapoor, Thomas Wolfers, Christian F. Baumgartner

Institutions: Universitätsklinikum Tübingen, Tübingen AI Center, University of Tübingen, University of Lucerne

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,170