DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data

arXiv:2310.13349v3 Announce Type: replace-cross
Abstract: Voxel-based multiple testing is widely used in neuroimaging data analysis. Traditional false discovery rate (FDR) control methods often ignore the spatial dependence among the voxel-based tests and thus suffer from substantial loss of testing power. While recent spatial FDR control methods have emerged, their validity and optimality remain questionable when handling the complex spatial dependencies of the brain. Concurrently, deep learning methods have revolutionized image segmentation, a task closely related to voxel-based multiple testing. In …

abstract analysis arxiv control cs.cv cs.lg data data analysis deep learning discovery false fdr loss multiple neuroimaging power rate spatial stat.ml testing tests type voxel