Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
Corrupted, inconsistent, or anomalous data silently threatens the safety and reliability of medical AI. Despite growing regulatory recognition of dataset quality assurance (QA) for high-risk medical AI, scalable automated detection remains underdeveloped. We employ unsupervised anomaly detection (AD) and out-of-distribution (OOD) detection as an automated dataset QA mechanism for multi-center dynamic contrast-enhanced breast MRI. We build a controlled AD benchmark of 17 realistic QA-relevant anomaly types from six public datasets (protocol violations, processing errors, incorrect anatomical regions) and propose a taxonomy of radiological image anomalies based on human visual perception, e...
| Authors | Chiara Tappermann, Steffen Renisch, Lars Ole Schwen et al. |
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| Primary category | cs.CV |
| Published | 17 August 2026 |
| Last updated at source | 16 September 2026 |
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