  Histopathology refers to the examination by a pathologist of biopsy samples.
Histopathology images are captured by a microscope to locate, examine, and
classify many diseases, such as different cancer types. They provide a detailed
view of different types of diseases and their tissue status. These images are
an essential resource with which to define biological compositions or analyze
cell and tissue structures. This imaging modality is very important for
diagnostic applications. The analysis of histopathology images is a prolific
and relevant research area supporting disease diagnosis. In this paper, the
challenges of histopathology image analysis are evaluated. An extensive review
of conventional and deep learning techniques which have been applied in
histological image analyses is presented. This review summarizes many current
datasets and highlights important challenges and constraints with recent deep
learning techniques, alongside possible future research avenues. Despite the
progress made in this research area so far, it is still a significant area of
open research because of the variety of imaging techniques and disease-specific
characteristics.
