ArticleiScience2023
Anatomy-aware self-supervised learning for anomaly detection in chest radiographs.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- AUCp: Pseudo-AUC for Inference Model Selection With Unlabeled Validation Data in Abnormality Detection.IEEE transactions on medical imaging · 2026Article
- Unsupervised Anomaly Detection in Medical Imaging: A Survey of Methods, Challenges, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Article
- DINO-LG: Enhancing vision transformers with label guidance for coronary artery calcium detection.Medical & biological engineering & computing · 2026Article
- Dual-domain adversarial learning and feature constraint for unsupervised anomaly detection in chest X-rays.Frontiers in medicine · 2026Article
- Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images.IEEE transactions on pattern analysis and machine intelligence · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we present a self-supervised learning (SSL)-based model that enables anatomical structure-based unsupervised anomaly detection (UAD). The model employs an anatomy-aware pasting (AnatPaste) augmentation tool that uses a threshold-based lung segmentation pretext task to create anomalies in normal chest radiographs used for model pretraining. These anomalies are similar to real anomalies and help the model recognize them. We evaluate our model using three open-source chest radiograph datasets. Our model exhibits area under curves of 92.1%, 78.7%, and 81.9%, which are the highest among those of existing UAD models. To the best of our knowledge, this is the first SSL model to employ anatomical information from segmentation as a pretext task. The performance of AnatPaste shows that incorporating anatomical information into SSL can effectively improve accuracy.
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Registered trials
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