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.

翻译：我们提出了“深度空间上下文”（DSCon）方法，该方法利用空间上下文概念来研究基于注意力的视觉模型。其灵感来源于组织病理学，但该方法可应用于多个领域。DSCon通过三种空间上下文度量：$SCM_{特征}$、$SCM{目标}$、$SCM_{残差}$，分别量化空间上下文在相邻区域特征、目标值（注意力分数）或残差中的可观测程度，这是通过将空间回归集成到流程中实现的。DSCon有助于验证研究问题。实验表明，肿瘤病变分类中的空间关系远大于正常组织。此外，发现空间回归中考虑的邻域尺寸越大，上下文信息的价值越低。进一步观察到，与目标和残差空间相比，特征空间中的空间上下文度量值最大。