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Deep convolutional neural networks for mammography: advances, challenges and applications

Overview of attention for article published in BMC Bioinformatics, June 2019
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
109 Dimensions

Readers on

mendeley
242 Mendeley
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Title
Deep convolutional neural networks for mammography: advances, challenges and applications
Published in
BMC Bioinformatics, June 2019
DOI 10.1186/s12859-019-2823-4
Pubmed ID
Authors

Dina Abdelhafiz, Clifford Yang, Reda Ammar, Sheida Nabavi

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 242 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 242 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 42 17%
Researcher 27 11%
Student > Ph. D. Student 27 11%
Student > Bachelor 27 11%
Lecturer 11 5%
Other 28 12%
Unknown 80 33%
Readers by discipline Count As %
Computer Science 66 27%
Engineering 22 9%
Medicine and Dentistry 18 7%
Biochemistry, Genetics and Molecular Biology 8 3%
Nursing and Health Professions 6 2%
Other 31 13%
Unknown 91 38%

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 30 July 2019.
All research outputs
#9,430,079
of 15,549,197 outputs
Outputs from BMC Bioinformatics
#3,684
of 5,671 outputs
Outputs of similar age
#146,452
of 267,963 outputs
Outputs of similar age from BMC Bioinformatics
#19
of 45 outputs
Altmetric has tracked 15,549,197 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,671 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 34th percentile – i.e., 34% 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 267,963 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 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 57% of its contemporaries.