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Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images

Overview of attention for article published in BMC Systems Biology, April 2018
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Mentioned by

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1 X user

Citations

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60 Dimensions

Readers on

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100 Mendeley
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Title
Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images
Published in
BMC Systems Biology, April 2018
DOI 10.1186/s12918-018-0572-z
Pubmed ID
Authors

Min Fu, Wenming Wu, Xiafei Hong, Qiuhua Liu, Jialin Jiang, Yaobin Ou, Yupei Zhao, Xinqi Gong

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 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 15%
Unspecified 13 13%
Student > Master 12 12%
Researcher 11 11%
Student > Bachelor 6 6%
Other 12 12%
Unknown 31 31%
Readers by discipline Count As %
Medicine and Dentistry 18 18%
Computer Science 17 17%
Unspecified 13 13%
Engineering 12 12%
Chemistry 2 2%
Other 4 4%
Unknown 34 34%
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 28 May 2019.
All research outputs
#20,572,330
of 23,149,216 outputs
Outputs from BMC Systems Biology
#1,011
of 1,144 outputs
Outputs of similar age
#287,736
of 326,666 outputs
Outputs of similar age from BMC Systems Biology
#33
of 47 outputs
Altmetric has tracked 23,149,216 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 1,144 research outputs from this source. They receive a mean Attention Score of 3.6. 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 326,666 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 47 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.