Pipeline-Invariant Representation Learning for Neuroimaging. (arXiv:2208.12909v2 [cs.LG] UPDATED)

Deep learning has been widely applied in neuroimaging, including predicting
brain-phenotype relationships from magnetic resonance imaging (MRI) volumes.
MRI data usually requires extensive preprocessing prior to modeling, but
variation introduced by different MRI preprocessing pipelines may lead to
different scientific findings, even when using the identical data. Motivated by
the data-centric perspective, we first evaluate how preprocessing pipeline
selection can impact the downstream performance of a supervised learning model.
We next propose two pipeline-invariant representation learning methodologies,
MPSL and PXL, …

arxiv neuroimaging pipeline representation representation learning