  Histopathological images contain rich phenotypic information that can be used
to monitor underlying mechanisms contributing to diseases progression and
patient survival outcomes. Recently, deep learning has become the mainstream
methodological choice for analyzing and interpreting cancer histology images.
In this paper, we present a comprehensive review of state-of-the-art deep
learning approaches that have been used in the context of histopathological
image analysis. From the survey of over 130 papers, we review the fields
progress based on the methodological aspect of different machine learning
strategies such as supervised, weakly supervised, unsupervised, transfer
learning and various other sub-variants of these methods. We also provide an
overview of deep learning based survival models that are applicable for
disease-specific prognosis tasks. Finally, we summarize several existing open
datasets and highlight critical challenges and limitations with current deep
learning approaches, along with possible avenues for future research.
