用混合异常检测提升生成式AI在医学影像中的可靠性
Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection
- 结合多种方法实现生成图像异常实时检测
- 在两个应用中显著降低人工审核需求
- 适合医疗AI研发与合规性要求高的场景
生成式AI在核医学中具有自动化和增强数据合成的巨大潜力。然而，生物医学影像的高风险特性要求具备稳健的机制来检测和管理模型的意外或错误行为。本文介绍了在BIOEMTECH's eyes(TM)系统中开发并实施的一种混合异常检测框架，以保障GenAI模型的安全运行。演示了两个应用场景：Pose2Xray，从鼠类照片生成合成X光片；DosimetrEYE，从二维SPECT/CT扫描估计三维辐射剂量图。在两种情况下，异常检测（OD）均提升了系统可靠性，减少了人工干预，并支持实时质量控制。该方法通过增强鲁棒性、可扩展性和监管合规性，提高了生成式AI在临床前环境中的工业可行性。
原文摘要 · Abstract (English)
Generative AI holds great potentials to automate and enhance data synthesis in nuclear medicine. However, the high-stakes nature of biomedical imaging necessitates robust mechanisms to detect and manage unexpected or erroneous model behavior. We introduce development and implementation of a hybrid anomaly detection framework to safeguard GenAI models in BIOEMTECH's eyes(TM) systems. Two applications are demonstrated: Pose2Xray, which generates synthetic X-rays from photographic mouse images, and DosimetrEYE, which estimates 3D radiation dose maps from 2D SPECT/CT scans. In both cases, our outlier detection (OD) enhances reliability, reduces manual oversight, and supports real-time quality control. This approach strengthens the industrial viability of GenAI in preclinical settings by increasing robustness, scalability, and regulatory compliance.
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