  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.
