Machine Learning Methods for Histopathological Image Analysis: A Review
This review addresses the challenge of accelerating and standardizing histopathological image analysis for cancer diagnosis, which is currently time-consuming and prone to human disagreement.
This paper reviews machine learning methods, including shallow and deep learning, applied to histopathological image analysis. It covers common tasks like segmentation and feature extraction, and lists publicly available and private datasets used in this research area.
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.