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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.07923 |
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| _version_ | 1866918122115563520 |
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| author | Binda, Jakub Paneta, Valentina Eleftheriadis, Vasileios Chung, Hongkyou Papadimitroulas, Panagiotis Chung, Neo Christopher |
| author_facet | Binda, Jakub Paneta, Valentina Eleftheriadis, Vasileios Chung, Hongkyou Papadimitroulas, Panagiotis Chung, Neo Christopher |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection Binda, Jakub Paneta, Valentina Eleftheriadis, Vasileios Chung, Hongkyou Papadimitroulas, Panagiotis Chung, Neo Christopher Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 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. |
| title | Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2508.07923 |