  The histopathology analysis is of great significance for the diagnosis and
prognosis of cancers, however, it has great challenges due to the enormous
heterogeneity of gigapixel whole slide images (WSIs) and the intricate
representation of pathological features. However, recent methods have not
adequately exploited geometrical representation in WSIs which is significant in
disease diagnosis. Therefore, we proposed a novel weakly-supervised framework,
Geometry-Aware Transformer (GOAT), in which we urge the model to pay attention
to the geometric characteristics within the tumor microenvironment which often
serve as potent indicators. In addition, a context-aware attention mechanism is
designed to extract and enhance the morphological features within WSIs.
