Pipeline-Invariant Representation Learning for Neuroimaging

Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey M. Plis, Vince D. Calhoun

Abstract: 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, to improve consistency in classification performance and to capture similar neural network representations. Using 2000 human subjects from the UK Biobank dataset, we demonstrate that both models present unique advantages, in particular that MPSL can be used to improve out-of-sample generalization to new pipelines, while PXL can be used to improve predictive performance consistency and representational similarity. These results suggest that our proposed models can be applied to overcome pipeline-related biases, and to improve prediction consistency and robustness in brain-phenotype modeling.