Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection
This work addresses the need for robust safeguards in high-stakes biomedical imaging to support industrial viability and regulatory compliance, though it appears incremental in applying existing anomaly detection methods to new domains.
The paper tackles the problem of ensuring reliability in generative AI applications for preclinical imaging by developing a hybrid anomaly detection framework, which enhances robustness and reduces manual oversight in two specific applications: Pose2Xray and DosimetrEYE.
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.