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UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients

Overview of attention for article published in BMC Medical Imaging, November 2021
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

news
1 news outlet

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
17 Mendeley
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Title
UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients
Published in
BMC Medical Imaging, November 2021
DOI 10.1186/s12880-021-00704-2
Pubmed ID
Authors

Rui Miao, Xin Dong, Sheng-Li Xie, Yong Liang, Sio-Long Lo

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 12%
Student > Bachelor 2 12%
Librarian 1 6%
Student > Doctoral Student 1 6%
Student > Master 1 6%
Other 1 6%
Unknown 9 53%
Readers by discipline Count As %
Medicine and Dentistry 2 12%
Environmental Science 2 12%
Arts and Humanities 1 6%
Computer Science 1 6%
Business, Management and Accounting 1 6%
Other 0 0%
Unknown 10 59%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 November 2021.
All research outputs
#6,336,103
of 22,543,472 outputs
Outputs from BMC Medical Imaging
#79
of 581 outputs
Outputs of similar age
#164,129
of 520,752 outputs
Outputs of similar age from BMC Medical Imaging
#11
of 80 outputs
Altmetric has tracked 22,543,472 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 581 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 520,752 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.