TABLeT uses a pre-trained 2D natural image autoencoder to tokenize 3D fMRI volumes into compact continuous tokens, enabling efficient long-range transformer modeling that outperforms voxel-based baselines on UKB, HCP, and ADHD-200 while using less memory.
Uk biobank: an open access re- source for identifying the causes of a wide range of complex diseases of middle and old age.PLoS Med., 12(3):e1001779
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Can Natural Image Autoencoders Compactly Tokenize fMRI Volumes for Long-Range Dynamics Modeling?
TABLeT uses a pre-trained 2D natural image autoencoder to tokenize 3D fMRI volumes into compact continuous tokens, enabling efficient long-range transformer modeling that outperforms voxel-based baselines on UKB, HCP, and ADHD-200 while using less memory.