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Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology

Overview of attention for article published in Modern Pathology, January 2024
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#37 of 3,301)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
9 news outlets
blogs
1 blog
twitter
6 X users

Readers on

mendeley
1 Mendeley
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Title
Tissue Contamination Challenges the Credibility of Machine Learning Models in Real World Digital Pathology
Published in
Modern Pathology, January 2024
DOI 10.1016/j.modpat.2024.100422
Pubmed ID
Authors

Ismail Irmakci, Ramin Nateghi, Rujoi Zhou, Mariavittoria Vescovo, Madeline Saft, Ashley E Ross, Ximing J Yang, Lee A D Cooper, Jeffery A Goldstein

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 69. 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 February 2024.
All research outputs
#634,347
of 25,779,988 outputs
Outputs from Modern Pathology
#37
of 3,301 outputs
Outputs of similar age
#9,856
of 354,044 outputs
Outputs of similar age from Modern Pathology
#2
of 23 outputs
Altmetric has tracked 25,779,988 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,301 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. 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 354,044 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 23 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 91% of its contemporaries.