Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays
This work addresses the challenge of distinguishing normal anatomical variations from pathological anomalies in medical imaging, which is crucial for healthcare applications, but it is incremental as it builds on existing diffusion-based methods by adding EHR integration.
The authors tackled the problem of unsupervised anomaly detection in chest X-rays by integrating structured Electronic Health Records (EHRs) with imaging features, resulting in Diff3M achieving state-of-the-art performance on CheXpert and MIMIC-CXR/IV datasets.
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