Abstract: Histopathological images (HIs) are the gold standard for evaluating some
types of tumors for cancer diagnosis. The analysis of such images is not only
time and resource consuming, but also very challenging even for experienced
pathologists, resulting in inter- and intra-observer disagreements. One of the
ways of accelerating such an analysis is to use computer-aided diagnosis (CAD)
systems. In this paper, we present a review on machine learning methods for
histopathological image analysis, including shallow and deep learning methods.
We also cover the most common tasks in HI analysis, such as segmentation and
feature extraction. In addition, we present a list of publicly available and
private datasets that have been used in HI research.