Online Unsupervised Learning For Domain Shift In Covid-19 CT Scan Datasets

Ewen, Nicolas; Khan, Naimul

doi:10.1109/icas49788.2021.9551146

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Ewen et al [ 59 ] suggested online unsupervised learning approach for COVID-19-CT-scan image classification. The components of online unsupervised learning include online machine learning and unsupervised learning.…”

Section: Machine Learningmentioning

confidence: 99%

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment

Hasan

Islam

Sadeq

et al. 2023

Sensors

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Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence’s role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients’ mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.

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Section: Machine Learningmentioning

confidence: 99%

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment

Hasan

Islam

Sadeq

et al. 2023

Sensors

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“…While Online Unsupervised Domain Adaptation has been explored for other AI tasks [6,17,23,29,30,39,47], it was first defined for the field of person ReID in [33].…”

Section: Online Unsupervised Domain Adaptationmentioning

confidence: 99%

Real-time online unsupervised domain adaptation for real-world person re-identification

Neff,

Danesh Pazho,

Tabkhi

2023

J Real-Time Image Proc

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Following the popularity of Unsupervised Domain Adaptation (UDA) in person re-identification, the recently proposed setting of Online Unsupervised Domain Adaptation (OUDA) attempts to bridge the gap towards practical applications by introducing a consideration of streaming data. However, this still falls short of truly representing real-world applications. This paper defines the setting of Real-world Real-time Online Unsupervised Domain Adaptation (R 2 OUDA) for Person Re-identification. The R 2 OUDA setting sets the stage for true real-world real-time OUDA, bringing to light four major limitations found in real-world applications that are often neglected in current research: system generated person images, subset distribution selection, time-based data stream segmentation, and a segment-based time constraint. To address all aspects of this new R 2 OUDA setting, this paper further proposes Real-World Real-Time Online Streaming Mutual Mean-Teaching (R 2 MMT), a novel multi-camera system for real-world person re-identification. Taking a popular person re-identification dataset, R 2 MMT was used to construct over 100 data subsets and train more than 3000 models, exploring the breadth of the R 2 OUDA setting to understand the training time and accuracy trade-offs and limitations for real-world applications.

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