REVIEW 2 cited by
COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 2 Pith papers
-
Assessing Risk of Stealing Proprietary Models for Medical Imaging Tasks
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.
-
Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor
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.
Discussion (0). Continue with ORCID to comment.