To detect distinctive structural abnormalities of schizophrenia early in magnetic resonance imaging (MRI) data, we propose a 3D Medical Image Global-Regional (3D-MIGR) Transformer with a VGG11BN backbone followed by a global-regional transformer encoder, which outperforms state-of-the-art models. We trained and tested our model on 887 pre-processed structural whole-head (WH) T1W 3D images from 3 datasets with similar acquisition parameters. 3D-MIGR Transformer improves AUROC to 0.990 and demonstrates strong generality. We found that combining volume-level contextual information with patch-level features enhances performance and allows us to identify ventricle areas as the most informative regions in regional schizophrenia likelihood visualization.
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