2019/08/24 by Samuel I. Berchuck, Berchuck, Samuel I., Felipe A. Medeiros +3
Computer Science · Mathematics · #AI in cancer detection #Applications (stat.AP) #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Machine Learning (stat.ML) #Morphological variations and asymmetry #stat.AP #stat.ML
paper · pdf · doi:10.48550/arxiv.1908.09195
This is a preprint of an article submitted for publication in the Annals of Applied Statistics. The article contains 26 pages and 7 figures
arxiv created 2019/08/24 · openalex publication_date 2019/08/24 · arxiv updated 2019/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well. While methods have been developed to account for high-dimensional spatial objects, the setting where there are exceedingly large samples of spatial observations has had less attention. The variational autoencoder (VAE), an unsupervised generative model based on deep learning and approximate Bayesian inference, fills this void using a latent variable specification that is inferred jointly across the large number of samples. In this manuscript, we compare the performance of the VAE with a more classical ST method when analyzing longitudinal visual fields from a large cohort of patients in a prospective glaucoma study. Through simulation and a case study, we demonstrate that the VAE is a scalable method for analyzing ST data, when the goal is to obtain accurate predictions. R code to implement the VAE can be found on GitHub: https://github.com/berchuck/vaeST.