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Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images

Overview of attention for article published in European Radiology Experimental, August 2019
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Mentioned by

twitter
1 tweeter

Citations

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9 Dimensions

Readers on

mendeley
31 Mendeley
Title
Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images
Published in
European Radiology Experimental, August 2019
DOI 10.1186/s41747-019-0112-7
Pubmed ID
Authors

Eduardo Fleury, Karem Marcomini

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Other 4 13%
Student > Ph. D. Student 4 13%
Student > Doctoral Student 3 10%
Student > Master 3 10%
Other 2 6%
Unknown 9 29%
Readers by discipline Count As %
Medicine and Dentistry 11 35%
Computer Science 3 10%
Physics and Astronomy 3 10%
Nursing and Health Professions 1 3%
Social Sciences 1 3%
Other 1 3%
Unknown 11 35%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 December 2019.
All research outputs
#14,544,795
of 16,488,620 outputs
Outputs from European Radiology Experimental
#118
of 127 outputs
Outputs of similar age
#316,767
of 381,429 outputs
Outputs of similar age from European Radiology Experimental
#31
of 36 outputs
Altmetric has tracked 16,488,620 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 127 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 381,429 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.