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arXiv · 2508.07923

Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection

Jakub Binda·Valentina Paneta·Vasileios Eleftheriadis·Hongkyou Chung·Panagiotis Papadimitroulas·Neo Christopher Chung
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Abstract

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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Jakub Binda, Valentina Paneta, Vasileios Eleftheriadis, Hongkyou Chung, Panagiotis Papadimitroulas, Neo Christopher Chung. 2025-08-11. Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection. https://arxiv.org/abs/2508.07923

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