  Histopathologic Images (HI) are the gold standard for evaluation of some
tumors. However, the analysis of such images is challenging even for
experienced pathologists, resulting in problems of inter and intra observer.
Besides that, the analysis is time and resource consuming. One of the ways to
accelerate such an analysis is by using Computer Aided Diagnosis systems. In
this work we present a literature review about the computing techniques to
process HI, including shallow and deep methods. We cover the most common tasks
for processing HI such as segmentation, feature extraction, unsupervised
learning and supervised learning. A dataset section show some datasets found
during the literature review. We also bring a study case of breast cancer
classification using a mix of deep and shallow machine learning methods. The
proposed method obtained an accuracy of 91% in the best case, outperforming the
compared baseline of the dataset.
