  Histopathology images; microscopy images of stained tissue biopsies contain
fundamental prognostic information that forms the foundation of pathological
analysis and diagnostic medicine. However, diagnostics from histopathology
images generally rely on a visual cognitive assessment of tissue slides which
implies an inherent element of interpretation and hence subjectivity. Access to
digitized histopathology images enabled the development of computational
systems aiming at reducing manual intervention and automating parts of
pathologists' workflow. Specifically, applications of deep learning to
histopathology image analysis now offer opportunities for better quantitative
modeling of disease appearance and hence possibly improved prediction of
disease aggressiveness and patient outcome. However digitized histopathology
tissue slides are unique in a variety of ways and come with their own set of
computational challenges. In this survey, we summarize the different challenges
facing computational systems for digital pathology and provide a review of
state-of-the-art works that developed deep learning-based solutions for the
predictive modeling of histopathology images from a detection, stain
normalization, segmentation, and tissue classification perspective. We then
discuss the challenges facing the validation and integration of such deep
learning-based computational systems in clinical workflow and reflect on future
opportunities for histopathology derived image measurements and better
predictive modeling.
