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Background
Manual brain extraction from magnetic resonance (MR) images is time‐consuming and prone to intra‐ and inter‐rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipelines. However, these methods tend to reduce their performance in unseen magnetic resonance imaging (MRI) scanner vendors and different imaging protocols...