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Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information

Overview of attention for article published in Frontiers in Neuroscience, January 2020
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1 X user

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74 Mendeley
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Title
Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information
Published in
Frontiers in Neuroscience, January 2020
DOI 10.3389/fnins.2019.01449
Pubmed ID
Authors

Po-Yu Kao, Shailja Shailja, Jiaxiang Jiang, Angela Zhang, Amil Khan, Jefferson W. Chen, B. S. Manjunath

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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.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 74 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 22%
Researcher 7 9%
Student > Master 6 8%
Student > Bachelor 4 5%
Student > Doctoral Student 4 5%
Other 7 9%
Unknown 30 41%
Readers by discipline Count As %
Computer Science 15 20%
Medicine and Dentistry 9 12%
Engineering 8 11%
Business, Management and Accounting 1 1%
Psychology 1 1%
Other 3 4%
Unknown 37 50%
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 03 February 2020.
All research outputs
#24,025,250
of 26,744,825 outputs
Outputs from Frontiers in Neuroscience
#10,630
of 12,044 outputs
Outputs of similar age
#413,400
of 482,793 outputs
Outputs of similar age from Frontiers in Neuroscience
#291
of 306 outputs
Altmetric has tracked 26,744,825 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 12,044 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. 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 482,793 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 306 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.