  With the remarkable success of representation learning for prediction
problems, we have witnessed a rapid expansion of the use of machine learning
and deep learning for the analysis of digital pathology and biopsy image
patches. However, learning over patch-wise features using convolutional neural
networks limits the ability of the model to capture global contextual
information and comprehensively model tissue composition. The phenotypical and
topological distribution of constituent histological entities play a critical
role in tissue diagnosis. As such, graph data representations and deep learning
have attracted significant attention for encoding tissue representations, and
capturing intra- and inter- entity level interactions. In this review, we
provide a conceptual grounding for graph analytics in digital pathology,
including entity-graph construction and graph architectures, and present their
current success for tumor localization and classification, tumor invasion and
staging, image retrieval, and survival prediction. We provide an overview of
these methods in a systematic manner organized by the graph representation of
the input image, scale, and organ on which they operate. We also outline the
limitations of existing techniques, and suggest potential future research
directions in this domain.
