A survey on artificial intelligence in histopathology image analysis

Abdelsamea, Mohammed M. and Zidan, Usama and Senousy, Zakaria and Gaber, Mohamed Medhat and Rakha, Emad and Ilyas, Mohammad (2022) A survey on artificial intelligence in histopathology image analysis. WIREs Data Mining and Knowledge Discovery, 12 (6). e1474. ISSN 1942-4787

WIREs Data Min Knowl - 2022 - Abdelsamea - A survey on artificial intelligence in histopathology image analysis (2).pdf - Published Version
Available under License Creative Commons Attribution.

Download (33MB)


The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field.

Item Type: Article
Identification Number: https://doi.org/10.1002/widm.1474
22 June 2022Accepted
27 July 2022Published Online
Uncontrolled Keywords: actionability, artificial intelligence, deep learning, histopathology, image analysis, machine learning
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Mohamed Gaber
Date Deposited: 12 Dec 2022 15:25
Last Modified: 12 Dec 2022 15:25
URI: https://www.open-access.bcu.ac.uk/id/eprint/14007

Actions (login required)

View Item View Item


In this section...