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A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2019
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1 X user

Citations

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31 Dimensions

Readers on

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37 Mendeley
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Title
A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images
Published in
BMC Medical Informatics and Decision Making, December 2019
DOI 10.1186/s12911-019-0988-4
Pubmed ID
Authors

Vitoantonio Bevilacqua, Antonio Brunetti, Giacomo Donato Cascarano, Andrea Guerriero, Francesco Pesce, Marco Moschetta, Loreto Gesualdo

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 16%
Student > Doctoral Student 4 11%
Student > Master 4 11%
Researcher 3 8%
Professor > Associate Professor 2 5%
Other 3 8%
Unknown 15 41%
Readers by discipline Count As %
Medicine and Dentistry 8 22%
Computer Science 6 16%
Engineering 4 11%
Psychology 1 3%
Physics and Astronomy 1 3%
Other 1 3%
Unknown 16 43%
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 13 December 2019.
All research outputs
#20,594,080
of 23,182,015 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,828
of 2,016 outputs
Outputs of similar age
#384,226
of 459,471 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#53
of 70 outputs
Altmetric has tracked 23,182,015 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 2,016 research outputs from this source. They receive a mean Attention Score of 4.9. 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 459,471 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 70 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.