Keywords: Machine Learning/Artificial Intelligence, Brain, Deep LearningSchizophrenia is a neurological disorder that requires accurate and rapid detection for earlier intervention. Previous explorations in artificial intelligence showed overwhelming performance using deep learning in schizophrenia classification, though the generalization remained a challenge. We propose our 3D Multi-scale Transformer (MST) using T1W structural MRI data to detect schizophrenia. By synthesizing reconstructed images at different scales, the transformer-based architecture improves robustness to generalize in unseen data. The proposed method reaches the same-level performance of AUROC to the benchmark mark model in schizophrenia identification, and performs better in all leave-one-site-out generality tests.
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