Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. (May 2022)

• Record Type: Journal Article
• Authors: Hassan, Haseeb; Ren, Zhaoyu; Zhou, Chengmin; Khan, Muazzam A.; Pan, Yi; Zhao, Jian; Huang, Bingding
• Is Part Of: Computer methods and programs in biomedicine. Volume 218(2022)
• Publication Date: 2022-05
• Subjects: COVID-19 CT detection -- COVID-19 CT diagnosis -- Supervised learning -- Weakly supervised learning -- COVID-19 CT deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106731
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Based on multi-level arrangements, an overview of deep supervised and weakly supervised COVID-19 CT approaches. Crucial information such as datasets, adopted frameworks, and key results are reported. Weak supervision has been adopted more extensively than supervised learning. Transfer learning is more effective by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity, and uncertainty quantification could be helpful for model reliability and efficacy. Abstract: Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.