Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI

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.