{"ID":2883718,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07923","arxiv_id":"2508.07923","title":"Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection","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.","short_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 fra...","url_abs":"https://arxiv.org/abs/2508.07923","url_pdf":"https://arxiv.org/pdf/2508.07923v1","authors":"[\"Jakub Binda\",\"Valentina Paneta\",\"Vasileios Eleftheriadis\",\"Hongkyou Chung\",\"Panagiotis Papadimitroulas\",\"Neo Christopher Chung\"]","published":"2025-08-11T12:35:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.HC\",\"cs.LG\"]","methods":"[]","has_code":false}