Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
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TL;DR - This study benchmarks unsupervised anomaly detection for automated quality assurance in multi-center breast MRI datasets. Domain-adapted methods achieved strong detection performance, but near-distribution anomalies and certain clinical conditions remain difficult.
- The benchmark covers 17 realistic anomaly types across six public datasets, including protocol violations, processing errors, and incorrect anatomy.
- A projection-based method with positional encoding achieved the highest AUROC (0.954).
- A 3D reconstruction method offered the best performance-generalization balance, reaching 0.936 AUROC.
- Hybrid OOD methods showed modality-specific failures; implants and mastectomies challenged every evaluated method.
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Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
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TL;DR - This study benchmarks unsupervised anomaly detection for automated quality assurance in multi-center breast MRI datasets. Domain-adapted methods achieved strong detection performance, but near-distribution anomalies and certain clinical conditions remain difficult.
- The benchmark covers 17 realistic anomaly types across six public datasets, including protocol violations, processing errors, and incorrect anatomy.
- A projection-based method with positional encoding achieved the highest AUROC (0.954).
- A 3D reconstruction method offered the best performance-generalization balance, reaching 0.936 AUROC.
- Hybrid OOD methods showed modality-specific failures; implants and mastectomies challenged every evaluated method.