Abstract:Purpose: Data augmentation improves the accuracy of deep learning models when training data is scarce by synthesizing additional samples. This work addresses the lack of validated augmentation methods specific for synthesizing anatomically realistic 4D (3D+time) images for neuroimaging, such as fMRI, by proposing a new augmentation method.
Materials and Methods: The proposed method, BLENDS, generates new nonlinear warp fields by combining intersubject coregistration maps, computed using symmetric normalizatio… Show more
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