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COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics

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arxiv 2103.10003 v2 pith:J653WP3N submitted 2021-03-18 eess.IV cs.CVcs.LG

classification eess.IVcs.CVcs.LG
keywords covid-19covidx-usdatasetultrasoundimagingopen-accesspandemicartificial
verification ladder T0 review T1 audit T2 compute T3 formal
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The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. Apart from the global health crises, the pandemic has also caused significant economic and financial difficulties and socio-physiological implications. Effective screening, triage, treatment planning, and prognostication of outcome plays a key role in controlling the pandemic. Recent studies have highlighted the role of point-of-care ultrasound imaging for COVID-19 screening and prognosis, particularly given that it is non-invasive, globally available, and easy-to-sanitize. Motivated by these attributes and the promise of artificial intelligence tools to aid clinicians, we introduce COVIDx-US, an open-access benchmark dataset of COVID-19 related ultrasound imaging data. The COVIDx-US dataset was curated from multiple sources and its current version, i.e., v1.2., consists of 150 lung ultrasound videos and 12,943 processed images of patients infected with COVID-19 infection, non-COVID-19 infection, other lung diseases/conditions, as well as normal control cases. The COVIDx-US is the largest open-access fully-curated dataset of its kind that has been systematically curated, processed, and validated specifically for the purpose of building and evaluating artificial intelligence algorithms and models.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Assessing Risk of Stealing Proprietary Models for Medical Imaging Tasks

    eess.IV 2025-06 conditional novelty 5.0 of 10

    Hard-label model stealing succeeds on medical image classifiers at a 5,000-query budget, and the proposed QueryWise method boosts clone accuracy for gallbladder cancer but not for COVID-19.

  2. Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor

    eess.IV 2025-01 conditional novelty 4.0 of 10

    MeDiVLAD, a pipeline using a DINO-pretrained ViT and dual-level VLAD aggregation, reports higher lung ultrasound severity scoring accuracy than supervised baselines on 283 videos.

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