Boosting AI-Generated Biomedical Images with Confidence through Advanced Statistical Inference

Music City Center 

Generative artificial intelligence (AI) has transformed biomedical imaging through synthetic image creation, addressing challenges of data availability, privacy, and dataset diversity in medical applications. In this paper, we propose a novel nonparametric method within the functional data framework to estimate signals and discern significant differences between the mean functions of real and synthetic biomedical imaging data, thereby enhancing model training. Focusing on surface-based synthetic imaging data, our approach employs spherical splines on triangulations to address spatial heterogeneity. A key innovation is our method for constructing simultaneous confidence corridors (SCCs), which quantify uncertainty in real-synthetic data differences and guide AI model refinement. We establish the asymptotic properties of our estimators and SCCs, proving that the SCCs are asymptotically equivalent to those constructed from noise-free images. Numerical studies validate the SCCs' coverage probability and demonstrate and our method's effectiveness in improving AI-driven image synthesis when integrated into generative AI training. The effectiveness of this approach is demonstrated through an application comparing original and synthetic cerebral spinal fluid functional MRI (cs-fMRI) data from the Human Connectome Project.