mlsanityData to 14 Oct 2026

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

arXiv 2505.17311 ↗First posted 22 May 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 22 May 2025.

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21 of 29 references.

References · 21

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  5. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  6. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  7. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  8. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  9. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  10. 2022Diffusion Models for Medical Anomaly Detectioncited by 232
  11. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
  12. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
9 more
  1. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 82
  2. 2023Anomaly Detection with Conditioned Denoising Diffusion Modelscited by 56
  3. 2024DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistencycited by 53
  4. 2021RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHRcited by 32
  5. 2024Diffusion Models with Implicit Guidance for Medical Anomaly Detectioncited by 26
  6. 2023Unsupervised Anomaly Detection in Medical Images Using Masked Diffusion Modelcited by 25
  7. 2023Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstructioncited by 18
  8. 2025MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairscited by 13
  9. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9

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