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Abstract
<p class="first" id="d3437508e90">Histopathological images contain rich phenotypic
information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes.
Recently, deep learning has become the mainstream methodological choice for analyzing
and interpreting 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
field's 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.
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