118 citations · 152 across the 7 of their papers we have counts for
3 papers · 1 filter
Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
Sami Hamdan, Bradley C. Love, Georg G. von Polier +4
Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine. To apply these approaches, confounds must first be remo…
Predictive Data Calibration for Linear Correlation Significance Testing
Kaustubh R. Patil, Simon B. Eickhoff, Robert Langner
Inferring linear relationships lies at the heart of many empirical investigations. A measure of linear dependence should correctly evaluate the strength of the relationship as well…
Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies
Di Wang, Nicolas Honnorat, Peter T. Fox +4
Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and h…