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Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

Overview of attention for article published in bioRxiv
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

twitter
143 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
14 Mendeley
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Title
Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media
Published in
bioRxiv
DOI 10.1101/396663
Authors

Schaumberg, Andrew J., Juarez-Nicanor, Wendy C., Choudhury, Sarah J., Pastrian, Laura G., Pritt, Bobbi S., Prieto Pozuelo, Mario, Sotillo Sanchez, Ricardo, Ho, Khanh, Zahra, Nusrat, Sener, Betul Duygu, Yip, Stephen, Xu, Bin, Annavarapu, Srinivas Rao, Morini, Aurelien, Jones, Karra A., Rosado-Orozco, Kathia, Mukhopadhyay, Sanjay, Miguel, Carlos, Yang, Hongyu, Rosen, Yale, Ali, Rola H., Folaranmi, Olaleke O., Gardner, Jerad M., Suleiman, Dauda E., Rusu, Corina, Stayerman, Celina, Gross, John, Sirintrapun, S. Joseph, Aly, Mariam, Fuchs, Thomas J.

Twitter Demographics

The data shown below were collected from the profiles of 143 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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 50%
Researcher 2 14%
Student > Master 2 14%
Student > Bachelor 1 7%
Other 1 7%
Other 0 0%
Unknown 1 7%
Readers by discipline Count As %
Computer Science 6 43%
Engineering 2 14%
Mathematics 1 7%
Physics and Astronomy 1 7%
Neuroscience 1 7%
Other 1 7%
Unknown 2 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 95. 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 29 March 2020.
All research outputs
#197,609
of 14,568,520 outputs
Outputs from bioRxiv
#1,037
of 77,126 outputs
Outputs of similar age
#7,283
of 273,951 outputs
Outputs of similar age from bioRxiv
#66
of 5,508 outputs
Altmetric has tracked 14,568,520 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 77,126 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.1. This one has done particularly well, scoring higher than 98% 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 273,951 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 97% of its contemporaries.
We're also able to compare this research output to 5,508 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.