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Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation

Overview of attention for article published in Frontiers in Computational Neuroscience, August 2019
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Mentioned by

twitter
3 X users

Readers on

mendeley
230 Mendeley
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Title
Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation
Published in
Frontiers in Computational Neuroscience, August 2019
DOI 10.3389/fncom.2019.00056
Pubmed ID
Authors

Guotai Wang, Wenqi Li, Sébastien Ourselin, Tom Vercauteren

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 230 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 230 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 18%
Researcher 29 13%
Student > Master 23 10%
Student > Bachelor 8 3%
Lecturer 7 3%
Other 22 10%
Unknown 100 43%
Readers by discipline Count As %
Computer Science 56 24%
Engineering 30 13%
Medicine and Dentistry 8 3%
Neuroscience 8 3%
Unspecified 3 1%
Other 15 7%
Unknown 110 48%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 April 2023.
All research outputs
#14,309,205
of 24,631,014 outputs
Outputs from Frontiers in Computational Neuroscience
#539
of 1,422 outputs
Outputs of similar age
#168,581
of 347,293 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#11
of 24 outputs
Altmetric has tracked 24,631,014 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,422 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has gotten more attention than average, scoring higher than 61% 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 347,293 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 50% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.