A0934
Title: Boosting AI-generated biomedical images with confidence through advanced statistical inference
Authors: Guannan Wang - College of William & Mary (United States) [presenting]
Zhiling Gu - Iowa State University (United States)
Shan Yu - University of Virginia (United States)
Lily Wang - George Mason University (United States)
Abstract: Generative artificial intelligence (AI) has transformed the biomedical imaging field through image synthesis, addressing challenges of data availability, privacy, and diversity in biomedical research. This project proposes a novel nonparametric method within the functional data framework to discern significant differences between the mean and covariance functions of original and synthetic biomedical imaging data, thereby enhancing the fidelity and utility of synthetic data. Focusing on surface-based synthetic imaging data, the approach employs triangulated spherical splines to address spatial heterogeneity. A key contribution is the construction of simultaneous confidence regions (SCRs) to rigorously quantify uncertainty in original-synthetic differences. The asymptotic properties of the proposed SCRs are established, providing exact coverage probabilities and demonstrating equivalence to those derived from noise-free imaging data. Simulation studies validate the coverage properties of the SCRs and evaluate the size and power of the associated hypothesis tests. The proposed method is applied to compare original and synthetic brain imaging data from the Human Connectome Project, highlighting significant differences between the original and synthetic images.
