Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6026989 | NeuroImage | 2014 | 11 Pages |
Abstract
We conclude that models trained on data acquired with a single scanner generalized well to data acquired with a different same-generation scanner even when the vendor differed. When confounding grouping and scanner during training is unavoidable to gather training data, regressing out inter-scanner and between-subject variability can reduce the loss in accuracy due to the confound.
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Authors
Daniel Kostro, Ahmed Abdulkadir, Alexandra Durr, Raymund Roos, Blair R. Leavitt, Hans Johnson, David Cash, Sarah J. Tabrizi, Rachael I. Scahill, Olaf Ronneberger, Stefan Klöppel, Track-HD Investigators Track-HD Investigators,