Confounder filetering (CF) method is an efficient method that can remove the influences of confounding
factors such as age or gender to improve the across-cohort prediction accuracy of neural
networks. One distinct advantage of CF is that it only requires minimal changes of
the baseline model’s architecture so that it can be plugged into most of the existing neural networks.