Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images
This work addresses the problem of detecting subtle brain changes for schizophrenia diagnosis, offering an incremental improvement in automated classification.
The study tackled automated schizophrenia diagnosis from structural brain MRI by introducing a Spatial Sequence Attention (SSA) mechanism, which outperformed existing methods like Squeeze & Excitation Network in classification tasks.
Schizophrenia is a debilitating, chronic mental disorder that significantly impacts an individual's cognitive abilities, behavior, and social interactions. It is characterized by subtle morphological changes in the brain, particularly in the gray matter. These changes are often imperceptible through manual observation, demanding an automated approach to diagnosis. This study introduces a deep learning methodology for the classification of individuals with Schizophrenia. We achieve this by implementing a diversified attention mechanism known as Spatial Sequence Attention (SSA) which is designed to extract and emphasize significant feature representations from structural MRI (sMRI). Initially, we employ the transfer learning paradigm by leveraging pre-trained DenseNet to extract initial feature maps from the final convolutional block which contains morphological alterations associated with Schizophrenia. These features are further processed by the proposed SSA to capture and emphasize intricate spatial interactions and relationships across volumes within the brain. Our experimental studies conducted on a clinical dataset have revealed that the proposed attention mechanism outperforms the existing Squeeze & Excitation Network for Schizophrenia classification.