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Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning

Overview of attention for article published in Nature Biomedical Engineering, March 2019
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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 (86th percentile)

Mentioned by

news
3 news outlets
blogs
2 blogs
twitter
146 X users
patent
11 patents
facebook
4 Facebook pages

Citations

dimensions_citation
452 Dimensions

Readers on

mendeley
460 Mendeley
Title
Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
Published in
Nature Biomedical Engineering, March 2019
DOI 10.1038/s41551-019-0362-y
Pubmed ID
Authors

Yair Rivenson, Hongda Wang, Zhensong Wei, Kevin de Haan, Yibo Zhang, Yichen Wu, Harun Günaydın, Jonathan E. Zuckerman, Thomas Chong, Anthony E. Sisk, Lindsey M. Westbrook, W. Dean Wallace, Aydogan Ozcan

X Demographics

X Demographics

The data shown below were collected from the profiles of 146 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 460 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 460 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 89 19%
Researcher 64 14%
Student > Master 34 7%
Student > Bachelor 28 6%
Student > Doctoral Student 25 5%
Other 78 17%
Unknown 142 31%
Readers by discipline Count As %
Engineering 100 22%
Computer Science 46 10%
Biochemistry, Genetics and Molecular Biology 34 7%
Medicine and Dentistry 33 7%
Physics and Astronomy 25 5%
Other 61 13%
Unknown 161 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 129. 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 27 February 2024.
All research outputs
#325,092
of 25,597,324 outputs
Outputs from Nature Biomedical Engineering
#205
of 1,147 outputs
Outputs of similar age
#7,299
of 368,271 outputs
Outputs of similar age from Nature Biomedical Engineering
#7
of 46 outputs
Altmetric has tracked 25,597,324 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,147 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 86.0. This one has done well, scoring higher than 82% 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 368,271 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 46 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.