Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays

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 URLthis https URL

@article{kim2025_2505.17311,
  title={ Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays },
  author={ Harim Kim and Yuhan Wang and Minkyu Ahn and Heeyoul Choi and Yuyin Zhou and Charmgil Hong },
  journal={arXiv preprint arXiv:2505.17311},
  year={ 2025 }
}