Machine Learning Methods for Histopathological Image Analysis: A Review

• URL: http://arxiv.org/abs/2102.03889v1
• Date: Sun, 7 Feb 2021 19:12:32 GMT
• Title: Machine Learning Methods for Histopathological Image Analysis: A Review
• Authors: Jonathan de Matos and Steve Tsham Mpinda Ataky and Alceu de Souza Britto Jr. and Luiz Eduardo Soares de Oliveira and Alessandro Lameiras Koerich
• Abstract summary: Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems.
• Score: 62.14548392474976
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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