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Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images Using Deep Learning

Overview of attention for article published in Frontiers in Neurology, July 2020
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2 X users

Citations

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24 Dimensions

Readers on

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31 Mendeley
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Title
Automatic Measurements of Fetal Lateral Ventricles in 2D Ultrasound Images Using Deep Learning
Published in
Frontiers in Neurology, July 2020
DOI 10.3389/fneur.2020.00526
Pubmed ID
Authors

Xijie Chen, Miao He, Tingting Dan, Nan Wang, Meifang Lin, Lihe Zhang, Jianbo Xian, Hongmin Cai, Hongning Xie

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

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 %
Student > Master 6 19%
Student > Bachelor 6 19%
Researcher 3 10%
Professor 2 6%
Student > Doctoral Student 1 3%
Other 5 16%
Unknown 8 26%
Readers by discipline Count As %
Computer Science 7 23%
Medicine and Dentistry 6 19%
Engineering 3 10%
Nursing and Health Professions 3 10%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 2 6%
Unknown 8 26%
Attention Score in Context

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 01 August 2020.
All research outputs
#18,737,061
of 23,225,652 outputs
Outputs from Frontiers in Neurology
#7,979
of 12,148 outputs
Outputs of similar age
#276,300
of 366,190 outputs
Outputs of similar age from Frontiers in Neurology
#452
of 466 outputs
Altmetric has tracked 23,225,652 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,148 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one is in the 24th percentile – i.e., 24% 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 366,190 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 466 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.