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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

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
32 Dimensions

Readers on

mendeley
151 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 8 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 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 151 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 26%
Researcher 34 23%
Student > Master 19 13%
Student > Bachelor 15 10%
Unspecified 14 9%
Other 30 20%
Readers by discipline Count As %
Engineering 30 20%
Computer Science 29 19%
Agricultural and Biological Sciences 28 19%
Biochemistry, Genetics and Molecular Biology 28 19%
Unspecified 17 11%
Other 19 13%

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
#3,311,853
of 13,305,734 outputs
Outputs from Scientific Reports
#19,876
of 64,044 outputs
Outputs of similar age
#78,993
of 267,760 outputs
Outputs of similar age from Scientific Reports
#50
of 195 outputs
Altmetric has tracked 13,305,734 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 64,044 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.6. This one has gotten more attention than average, scoring higher than 68% 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 267,760 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 70% of its contemporaries.
We're also able to compare this research output to 195 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 74% of its contemporaries.