A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haema...
A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides
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Author / Creator
Shen, Zhuoyan , Simard, Mikaël , Brand, Douglas , Andrei, Vanghelita , Al-Khader, Ali , Oumlil, Fatine , Trevers, Katherine , Butters, Thomas , Haefliger, Simon , Kara, Eleanna , Amary, Fernanda , Tirabosco, Roberto , Cool, Paul , Royle, Gary , Hawkins, Maria A. , Flanagan, Adrienne M. and Collins-Fekete, Charles-Antoine
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
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Publisher
London: Nature Publishing Group UK
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Contents
Mitotic activity is an important feature for grading several cancer types. However, counting mitotic figures (cells in division) is a time-consuming and laborious task prone to inter-observer variation. Inaccurate recognition of MFs can lead to incorrect grading and hence potential suboptimal treatment. This study presents an artificial intelligenc...
Alternative Titles
Full title
A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_862ddc50bb82412983a0b201e5558c21
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_862ddc50bb82412983a0b201e5558c21
Other Identifiers
ISSN
2399-3642
E-ISSN
2399-3642
DOI
10.1038/s42003-024-07398-6
