| 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
The data shown below were collected from the profiles of 147 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 38 | 26% |
| United Kingdom | 11 | 7% |
| France | 10 | 7% |
| Australia | 5 | 3% |
| Canada | 4 | 3% |
| India | 3 | 2% |
| Japan | 3 | 2% |
| Sweden | 3 | 2% |
| Brazil | 2 | 1% |
| Other | 14 | 10% |
| Unknown | 54 | 37% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 83 | 56% |
| Scientists | 52 | 35% |
| Science communicators (journalists, bloggers, editors) | 7 | 5% |
| Practitioners (doctors, other healthcare professionals) | 5 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 466 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 466 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 90 | 19% |
| Researcher | 64 | 14% |
| Student > Master | 34 | 7% |
| Student > Bachelor | 28 | 6% |
| Student > Doctoral Student | 25 | 5% |
| Other | 83 | 18% |
| Unknown | 142 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 101 | 22% |
| Computer Science | 46 | 10% |
| Biochemistry, Genetics and Molecular Biology | 34 | 7% |
| Medicine and Dentistry | 33 | 7% |
| Physics and Astronomy | 25 | 5% |
| Other | 66 | 14% |
| Unknown | 161 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 132. 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 18 April 2024.
All research outputs
#320,835
of 25,784,004 outputs
Outputs from Nature Biomedical Engineering
#199
of 1,154 outputs
Outputs of similar age
#7,133
of 368,875 outputs
Outputs of similar age from Nature Biomedical Engineering
#7
of 46 outputs
Altmetric has tracked 25,784,004 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 1,154 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 85.8. 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 368,875 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 84% of its contemporaries.