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.

翻译：精神分裂症是一种使人衰弱的慢性精神障碍，显著影响个体的认知能力、行为及社会交往。其特征是大脑，特别是灰质中细微的形态学变化。这些变化通常难以通过人工观察察觉，因此需要一种自动化的诊断方法。本研究引入了一种用于精神分裂症患者分类的深度学习方法。我们通过实现一种称为空间序列注意力的多样化注意力机制来实现这一目标，该机制旨在从结构磁共振成像中提取并强调显著的特征表示。首先，我们采用迁移学习范式，利用预训练的DenseNet从最终卷积块中提取初始特征图，该卷积块包含与精神分裂症相关的形态学改变。这些特征随后由提出的SSA进一步处理，以捕获并强调大脑内跨体积的复杂空间交互和关系。我们在临床数据集上进行的实验研究表明，所提出的注意力机制在精神分裂症分类任务上优于现有的Squeeze & Excitation Network。