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Analyzing huge pathology images with open source software

Overview of attention for article published in Diagnostic Pathology, June 2013
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Mentioned by

twitter
3 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
82 Dimensions

Readers on

mendeley
125 Mendeley
citeulike
1 CiteULike
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Title
Analyzing huge pathology images with open source software
Published in
Diagnostic Pathology, June 2013
DOI 10.1186/1746-1596-8-92
Pubmed ID
Authors

Christophe Deroulers, David Ameisen, Mathilde Badoual, Chloé Gerin, Alexandre Granier, Marc Lartaud

Abstract

Digital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology. However, such virtual slides build up a technical challenge since the images occupy often several gigabytes and cannot be fully opened in a computer's memory. Moreover, there is no standard format. Therefore, most common open source tools such as ImageJ fail at treating them, and the others require expensive hardware while still being prohibitively slow.

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

Geographical breakdown

Country Count As %
United States 3 2%
Netherlands 1 <1%
Germany 1 <1%
Denmark 1 <1%
France 1 <1%
Unknown 118 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 20%
Researcher 25 20%
Student > Master 15 12%
Student > Bachelor 9 7%
Professor > Associate Professor 8 6%
Other 21 17%
Unknown 22 18%
Readers by discipline Count As %
Medicine and Dentistry 37 30%
Agricultural and Biological Sciences 17 14%
Computer Science 13 10%
Engineering 9 7%
Neuroscience 5 4%
Other 20 16%
Unknown 24 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 12 November 2019.
All research outputs
#13,000,681
of 22,441,655 outputs
Outputs from Diagnostic Pathology
#321
of 1,113 outputs
Outputs of similar age
#89,868
of 176,789 outputs
Outputs of similar age from Diagnostic Pathology
#1
of 1 outputs
Altmetric has tracked 22,441,655 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,113 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 70% 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 176,789 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them