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Abstract #3916

Classification of Schizophrenia with Structural MRI Using 3D-MIGR Transformer

Ye Tian1, Junhao Zhang1, Xuemin Zhu1, Pin-Yu Lee1, Vishwanatha Mitnala Rao1, Zhuoyao Xin1, Andrew F Laine2, Scott A Small3, and Jia Guo4
1Columbia University, New York, NY, United States, 2Biomedical Engineering, Columbia University, New York, NY, United States, 3Neurology, Columbia University, New York, NY, United States, 4Psychiatry, Columbia University, New York, NY, United States


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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