GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies
GLACIAL:面向纵向影像研究的格兰杰与学习式因果分析
The Granger framework is useful for discovering causal relations in time-varying signals. However, most Granger causality (GC) methods are developed for densely sampled timeseries data. A substantially different setting, particularly common in medical imaging, is the longitudinal study design, where multiple subjects are followed and sparsely observed over time. Longitudinal studies commonly track several biomarkers, which are likely governed by nonlinear dynamics that might…