Abstract
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.
Abstract (translated)
精神分裂症是一种对个体认知能力、行为和社交互动产生显著影响的慢性精神障碍。其特征是大脑皮层的细微形态变化，特别是灰质。这些变化通常难以通过手动观察察觉，需要采用自动化的诊断方法。本研究介绍了一种用于精神分裂症分类的深度学习方法。我们通过实现一种名为Spatial Sequence Attention（SSA）的多样化注意力机制来实现这一目标，该机制旨在从结构MRI（sMRI）中提取并强调与精神分裂症相关的显著特征表示。首先，我们利用预训练的DenseNet，通过最终卷积 block 提取与精神分裂症相关的形态变化特征。然后，通过所提出的SSA对这些特征进行进一步处理，以捕捉和强调脑部各个体积之间复杂的空间交互和关系。我们对临床数据集进行的实验研究结果表明，所提出的注意力机制超过了现有精神分裂症分类器的表现。
URL
https://arxiv.org/abs/2406.12683
