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

Deep spatial context: when attention-based models meet spatial regression