Catching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning
arXiv:2605.24609v2 Announce Type: replace-cross Abstract: Artificial intelligence is increasingly integrated into radiotherapy workflows, yet such pipelines remain vulnerable to out-of-distribution image data that may introduce unexpected behavior in clinical tasks. Deep learning-based anomaly detection for pelvic magnetic resonance imaging (MRI) remains largely unexplored, and transparent evaluation of its feasibility for full automation is limited. We developed and evaluated a fully automated, unsupervised anomaly-detection framework for pelvic and brain MRI. A two-stage framework was traine
The increasing integration of AI into critical medical workflows necessitates robust anomaly detection to prevent catastrophic failures, making this research timely.
This development enhances the reliability and safety of AI applications in healthcare, bolstering trust and accelerating wider adoption across deep tech domains.
The ability to automatically detect and localize anomalies in medical imaging using deep learning reduces human error and improves patient safety in AI-supported radiotherapy.
- · Healthcare AI developers
- · Medical imaging companies
- · Radiotherapy patients
- · Deep learning researchers
- · Traditional image quality assurance methods
Increased confidence and adoption of AI in high-stakes medical procedures.
Acceleration of AI integration into other diagnostic and therapeutic medical fields, potentially reducing operational costs.
Ethical and regulatory frameworks adapting to autonomous AI systems capable of self-correction and anomaly flagging in critical applications.
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Read at arXiv cs.AI