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Spatial tissue imaging technologies enable highly resolved spatial characterization of cellular phenotypes. Today this spatial mapping still largely depends on laborious manual annotation and molecular labels to understand tissue organization. As a result, we are not optimally leveraging higher-order patterns of cell organization potentially connected to disease pathology or clinical outcomes. To address this gap, we propose a novel approach how autoencoders with spatial attention mechanism can be trained to enrich cell phenotyping. Our approach combines information on cellular phenotypes with the physical proximity of cells to accurately identify organ-specific microanatomical in the tumor microenvironment. We apply our method to lung tumor tissues imaging mass cytometry data to show how it can detect higher-level cell organizations and information on structural differences.
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