Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays
Journal:
arXiv
Published Date:
May 22, 2025
Abstract
Unsupervised anomaly detection (UAD) in medical imaging is crucial for
identifying pathological abnormalities without requiring extensive labeled
data. However, existing diffusion-based UAD models rely solely on imaging
features, limiting their ability to distinguish between normal anatomical
variations and pathological anomalies. To address this, we propose Diff3M, a
multi-modal diffusion-based framework that integrates chest X-rays and
structured Electronic Health Records (EHRs) for enhanced anomaly detection.
Specifically, we introduce a novel image-EHR cross-attention module to
incorporate structured clinical context into the image generation process,
improving the model's ability to differentiate normal from abnormal features.
Additionally, we develop a static masking strategy to enhance the
reconstruction of normal-like images from anomalies. Extensive evaluations on
CheXpert and MIMIC-CXR/IV demonstrate that Diff3M achieves state-of-the-art
performance, outperforming existing UAD methods in medical imaging. Our code is
available at this http URL https://github.com/nth221/Diff3M
