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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Chile | 1 | 13% |
| Norway | 1 | 13% |
| Switzerland | 1 | 13% |
| Unknown | 5 | 63% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 75% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
| Scientists | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 262 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 262 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 60 | 23% |
| Researcher | 40 | 15% |
| Student > Master | 32 | 12% |
| Student > Bachelor | 23 | 9% |
| Professor > Associate Professor | 13 | 5% |
| Other | 35 | 13% |
| Unknown | 59 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 45 | 17% |
| Computer Science | 44 | 17% |
| Biochemistry, Genetics and Molecular Biology | 38 | 15% |
| Agricultural and Biological Sciences | 30 | 11% |
| Physics and Astronomy | 11 | 4% |
| Other | 27 | 10% |
| Unknown | 67 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 10 January 2024.
All research outputs
#2,283,758
of 23,577,654 outputs
Outputs from Scientific Reports
#20,161
of 127,567 outputs
Outputs of similar age
#44,882
of 319,029 outputs
Outputs of similar age from Scientific Reports
#934
of 6,009 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 127,567 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.4. This one has done well, scoring higher than 84% 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 319,029 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 6,009 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.