Schizophrenia diagnosis is clinically difficult due to the lack of biomarkers associated with the disease. While machine learning algorithms and convolutional neural networks (CNNs) have found success using neuroimaging inputs to diagnose the disease, they have historically not performed or generalized well enough for clinical use. We propose 3D-MIC-Transformer, the first transformer-based deep learning architecture applied to neurological disease classification that demonstrates state-of-the-art schizophrenia classification performance and generalization using structural MRI inputs. 3D-MIC-Transformer outperforms prior CNN implementations (AUROC: 0.985, accuracy: 0.933), and we believe 3D-MIC-Transformer can serve as a backbone for other disease classification tasks in the future.
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