We propose 'Deep spatial context' (DSCon) method, which serves for
investigation of the attention-based vision models using the concept of spatial
context. It was inspired by histopathologists, however, the method can be
applied to various domains. The DSCon allows for a quantitative measure of the
spatial context's role using three Spatial Context Measures: $SCM_{features}$,
$SCM_{targets}$, $SCM_{residuals}$ to distinguish whether the spatial context
is observable within the features of neighboring regions, their target values
(attention scores) or residuals, respectively. It is achieved by integrating
spatial regression into the pipeline. The DSCon helps to verify research
questions. The experiments reveal that spatial relationships are much bigger in
the case of the classification of tumor lesions than normal tissues. Moreover,
it turns out that the larger the size of the neighborhood taken into account
within spatial regression, the less valuable contextual information is.
Furthermore, it is observed that the spatial context measure is the largest
when considered within the feature space as opposed to the targets and
residuals.

这篇研究提出了 “深度空间背景”（DSCon）方法，它利用空间背景的概念来研究基于注意力的视觉模型，在组织病理学家的启发下，该方法可以应用于各种领域。DSCon 通过将空间回归集成到流程中来定量衡量空间背景在特征、目标值（注意力得分）或残差的邻近区域特征中是否可观察，以此帮助验证研究问题。实验证明，在肿瘤病变的分类中，空间关系要比正常组织更重要。此外，研究还观察到，在空间回归中考虑的邻域大小越大，上下文信息的价值就越小。此外，观察到在特征空间中考虑的空间背景测量值最大，而目标值和残差中的空间背景测量值较小。