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Automated Training of Deep Convolutional Neural Networks for Cell Segmentation

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

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
139 Mendeley
Title
Automated Training of Deep Convolutional Neural Networks for Cell Segmentation
Published in
Scientific Reports, August 2017
DOI 10.1038/s41598-017-07599-6
Pubmed ID
Authors

Sajith Kecheril Sadanandan, Petter Ranefall, Sylvie Le Guyader, Carolina Wählby

Abstract

Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 139 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 27%
Researcher 30 22%
Student > Master 17 12%
Student > Bachelor 13 9%
Unspecified 12 9%
Other 29 21%
Readers by discipline Count As %
Engineering 29 21%
Computer Science 28 20%
Agricultural and Biological Sciences 27 19%
Biochemistry, Genetics and Molecular Biology 24 17%
Unspecified 15 11%
Other 16 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 04 December 2017.
All research outputs
#3,044,885
of 12,652,703 outputs
Outputs from Scientific Reports
#17,741
of 59,032 outputs
Outputs of similar age
#75,628
of 265,425 outputs
Outputs of similar age from Scientific Reports
#2
of 18 outputs
Altmetric has tracked 12,652,703 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 59,032 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one has gotten more attention than average, scoring higher than 69% 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 265,425 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.