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

• 成果类型: Article; Early Access
• 署名作者: Gu, Zhiling; Yu, Shan; Wang, Guannan; Wang, Lily
• 署名单位: Yale University; University of Virginia; George Mason University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2552510
• 发表日期: 2025
• 关键词: bands

摘要

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 article 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, our 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 the original and synthetic brain imaging data from the Human Connectome Project, where it highlights significant differences between original and synthetic images. We demonstrate that a straightforward transformation can align the mean and covariance functions of synthetic images with those of the original data, improving their reliability and utility for biomedical research applications. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.