A survey on artificial intelligence in histopathology image analysis

• Published in: Wiley interdisciplinary reviews. Data mining and knowledge discovery Vol. 12; no. 6
• Main Authors: Abdelsamea, Mohammed M., Zidan, Usama, Senousy, Zakaria, Gaber, Mohamed Medhat, Rakha, Emad, Ilyas, Mohammad
• Format: Journal Article
• Language: English
• Published: Hoboken, USA Wiley Periodicals, Inc 01.11.2022
• Subjects: Actionability; artificial intelligence; deep learning; histopathology; image analysis; machine learning
• ISSN: 1942-4787; 1942-4795; 1942-4795
• DOI: 10.1002/widm.1474

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. This article is categorized under: Application Areas > Health Care The main topics covered in this survey paper.