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Automated diagnosis of 7 canine skin tumors using machine learning on H

Overview of attention for article published in Veterinary Pathology, July 2023
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

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11 X users

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23 Mendeley
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Title
Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images
Published in
Veterinary Pathology, July 2023
DOI 10.1177/03009858231189205
Pubmed ID
Authors

Marco Fragoso-Garcia, Frauke Wilm, Christof A. Bertram, Sophie Merz, Anja Schmidt, Taryn Donovan, Andrea Fuchs-Baumgartinger, Alexander Bartel, Christian Marzahl, Laura Diehl, Chloe Puget, Andreas Maier, Marc Aubreville, Katharina Breininger, Robert Klopfleisch

Abstract

Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types.

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X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 13%
Professor > Associate Professor 1 4%
Other 1 4%
Student > Master 1 4%
Unknown 17 74%
Readers by discipline Count As %
Veterinary Science and Veterinary Medicine 2 9%
Linguistics 1 4%
Computer Science 1 4%
Immunology and Microbiology 1 4%
Unknown 18 78%
Attention Score in Context

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 05 August 2023.
All research outputs
#6,997,164
of 25,286,324 outputs
Outputs from Veterinary Pathology
#321
of 1,841 outputs
Outputs of similar age
#103,824
of 348,190 outputs
Outputs of similar age from Veterinary Pathology
#2
of 32 outputs
Altmetric has tracked 25,286,324 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,841 research outputs from this source. They receive a mean Attention Score of 4.5. 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 348,190 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 32 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 96% of its contemporaries.