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http://dx.doi.org/10.9718/JBER.2022.43.4.259

A review of Explainable AI Techniques in Medical Imaging  

Lee, DongEon (Department of Information Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University)
Park, ChunSu (Department of Information Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University)
Kang, Jeong-Woon (Department of Information Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University)
Kim, MinWoo (School of Biomedical Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University)
Publication Information
Journal of Biomedical Engineering Research / v.43, no.4, 2022 , pp. 259-270 More about this Journal
Abstract
Artificial intelligence (AI) has been studied in various fields of medical imaging. Currently, top-notch deep learning (DL) techniques have led to high diagnostic accuracy and fast computation. However, they are rarely used in real clinical practices because of a lack of reliability concerning their results. Most DL models can achieve high performance by extracting features from large volumes of data. However, increasing model complexity and nonlinearity turn such models into black boxes that are seldom accessible, interpretable, and transparent. As a result, scientific interest in the field of explainable artificial intelligence (XAI) is gradually emerging. This study aims to review diverse XAI approaches currently exploited in medical imaging. We identify the concepts of the methods, introduce studies applying them to imaging modalities such as computational tomography (CT), magnetic resonance imaging (MRI), and endoscopy, and lastly discuss limitations and challenges faced by XAI for future studies.
Keywords
Explainable AI; XAI; Medical imaging; Deep learning;
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