Monitoring medical imaging data is increasingly important in healthcare because image-derived quantitative features provide valuable information for assessing disease progression, diagnostic quality, and process stability. However, such data are often feature-rich, distributionally complex, and limited in sample size, and existing methods do not always provide a unified way for retrospectively monitoring joint changes in mean and covariance without relying on strong parametric assumptions. Motivated by these challenges, this paper develops a Phase I nonparametric change-point monitoring framework for individual observations. The framework combines existing high-dimensional two-sample statistics for mean and covariance changes into two control chart schemes, whose control limits are determined through simulation. A post-signal diagnostic procedure is also presented to estimate the change-point location, identify whether the detected shift occurs in the mean, the covariance, or both, and screen potentially affected variables. An extensive simulation study considering various distributional models shows that the proposed charts preserve the nominal in-control false alarm probability, achieve favorable out-of-control detection power, and yield informative post-signal diagnostic results. Finally, a real-world colposcopy dataset is used to illustrate the practical applicability of the proposed framework for the retrospective monitoring of medical imaging data.
Liu et al. (Fri,) studied this question.