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Segmenting Brain Tumor Using Cascaded V-Nets in Multimodal MR Images

Overview of attention for article published in Frontiers in Computational Neuroscience, February 2020
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  • Average Attention Score compared to outputs of the same age

Mentioned by

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4 X users

Citations

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44 Dimensions

Readers on

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60 Mendeley
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Title
Segmenting Brain Tumor Using Cascaded V-Nets in Multimodal MR Images
Published in
Frontiers in Computational Neuroscience, February 2020
DOI 10.3389/fncom.2020.00009
Pubmed ID
Authors

Rui Hua, Quan Huo, Yaozong Gao, He Sui, Bing Zhang, Yu Sun, Zhanhao Mo, Feng Shi

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 12%
Student > Bachelor 5 8%
Student > Master 5 8%
Lecturer 4 7%
Researcher 3 5%
Other 3 5%
Unknown 33 55%
Readers by discipline Count As %
Computer Science 10 17%
Engineering 5 8%
Psychology 2 3%
Medicine and Dentistry 2 3%
Neuroscience 2 3%
Other 2 3%
Unknown 37 62%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 May 2020.
All research outputs
#16,394,432
of 26,315,660 outputs
Outputs from Frontiers in Computational Neuroscience
#694
of 1,490 outputs
Outputs of similar age
#270,778
of 487,661 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#28
of 35 outputs
Altmetric has tracked 26,315,660 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,490 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one is in the 49th percentile – i.e., 49% 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 487,661 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.