DeepRepViz: Identifying Confounders in Deep Learning Model Predictions

arXiv:2309.15551v2 Announce Type: replace
Abstract: Deep Learning (DL) models have gained popularity in neuroimaging studies for predicting psychological behaviors, cognitive traits, and brain pathologies. However, these models can be biased by confounders such as age, sex, or imaging artifacts from the acquisition process. To address this, we introduce 'DeepRepViz', a two-part framework designed to identify confounders in DL model predictions. The first component is a visualization tool that can be used to qualitatively examine the final latent representation of the …

abstract acquisition age arxiv brain cognitive cs.ai cs.cv cs.lg deep learning framework however imaging neuroimaging part predictions process sex studies type