Machine Learning Methods for Histopathological Image Analysis: A Review

TL;DR

This review paper discusses machine learning techniques, including deep learning, used in analyzing histopathological images for cancer diagnosis, highlighting tasks like segmentation and feature extraction, and listing relevant datasets.

Contribution

It provides a comprehensive overview of machine learning methods applied to histopathological image analysis, including recent advances and available datasets.

Findings

Deep learning methods improve accuracy in HI analysis.

Various datasets facilitate research and benchmarking.

Machine learning accelerates histopathological diagnosis.

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.