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COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow
2021 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2021 ; 2021-September, 2021.
Article in English | Scopus | ID: covidwho-1511202
ABSTRACT
As the COVID-19 pandemic continues to devastate globally, one promising field of research is machine learning-driven computer vision to streamline various parts of the COVID-19 clinical workflow. These machine learning methods are typically stand-alone models designed without consideration for the integration necessary for real-world application workflows. In this study, we take a machine learning and systems (MLSys) perspective to design a system for COVID-19 patient screening with the clinical workflow in mind. The COVID-Net system is comprised of the continuously evolving COVIDx dataset, COVID-Net deep neural network for COVID-19 patient detection, and COVID-Net S deep neural networks for disease severity scoring for COVID-19 positive patient cases. The deep neural networks within the COVID-Net system possess state-of-the-art performance, and are designed to be integrated within a user interface (UI) for clinical decision support with automatic report generation to assist clinicians in their treatment decisions. © 2021 IEEE.

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 2021 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 2021 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2021 Year: 2021 Document Type: Article