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.

该研究引入了一种深度学习方法，使用空间序列注意力（SSA）机制对带有精神分裂症的个体进行分类，通过结构性磁共振成像（sMRI）提取并强调重要特征表征，实验证明该注意力机制在精神分裂症分类中优于现有的压缩和激励网络。

结构脑 MR 图像的精神分裂症分类空间序列注意力网络

Spatial Sequence Attention Network for Schizophrenia Classification from  Structural Brain MR Images

Recent research in deep learning methodology has led to a variety of complex
modelling techniques in computer vision (CV) that reach or even outperform
human performance. Although these black-box deep learning models have obtained
astounding results, they are limited in their interpretability and transparency
which are critical to take learning machines to the next step to include them
in sensitive decision-support systems involving human supervision. Hence, the
development of explainable techniques for computer vision (XCV) has recently
attracted increasing attention. In the realm of XCV, Class Activation Maps
(CAMs) have become widely recognized and utilized for enhancing
interpretability and insights into the decision-making process of deep learning
models. This work presents a comprehensive overview of the evolution of Class
Activation Map methods over time. It also explores the metrics used for
evaluating CAMs and introduces auxiliary techniques to improve the saliency of
these methods. The overview concludes by proposing potential avenues for future
research in this evolving field.

最近在深度学习方法学研究方面取得了一系列复杂的计算机视觉模型技术，达到甚至超过了人类的表现。然而，这些黑盒深度学习模型在可解释性和透明度方面存在一定限制，而这对将学习机器纳入涉及人类监督的敏感决策支持系统中是至关重要的。因此，解释性计算机视觉技术的发展近年来引起了越来越多的关注。在解释性计算机视觉领域中，类激活映射（CAMs）已被广泛认可和利用，以增强深度学习模型决策过程的可解释性和洞察力。本文全面概述了类激活映射方法随着时间推移的演变，并探讨了用于评估类激活映射的度量标准，并介绍了改善这些方法显著性的辅助技术。概述最后提出了未来研究的潜在方向。