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Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

Overview of attention for article published in Scientific Reports, April 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
17 news outlets
twitter
83 X users
patent
2 patents
facebook
1 Facebook page
wikipedia
1 Wikipedia page
reddit
2 Redditors

Citations

dimensions_citation
408 Dimensions

Readers on

mendeley
528 Mendeley
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Title
Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent
Published in
Scientific Reports, April 2017
DOI 10.1038/srep46450
Pubmed ID
Authors

Angel Cruz-Roa, Hannah Gilmore, Ajay Basavanhally, Michael Feldman, Shridar Ganesan, Natalie N.C. Shih, John Tomaszewski, Fabio A. González, Anant Madabhushi

X Demographics

X Demographics

The data shown below were collected from the profiles of 83 X users 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 528 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Norway 1 <1%
Unknown 525 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 81 15%
Student > Master 81 15%
Researcher 73 14%
Student > Bachelor 30 6%
Student > Doctoral Student 29 5%
Other 98 19%
Unknown 136 26%
Readers by discipline Count As %
Computer Science 131 25%
Medicine and Dentistry 77 15%
Engineering 59 11%
Biochemistry, Genetics and Molecular Biology 28 5%
Agricultural and Biological Sciences 17 3%
Other 54 10%
Unknown 162 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 173. 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 23 February 2024.
All research outputs
#240,714
of 25,958,626 outputs
Outputs from Scientific Reports
#2,822
of 144,156 outputs
Outputs of similar age
#4,944
of 327,697 outputs
Outputs of similar age from Scientific Reports
#99
of 4,244 outputs
Altmetric has tracked 25,958,626 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 144,156 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.9. This one has done particularly well, scoring higher than 98% 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 327,697 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 4,244 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.