Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hyb...
Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images
About this item
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Author / Creator
Xue, Tian , Chang, Heng , Ren, Min , Wang, Haochen , Yang, Yu , Wang, Boyang , Lv, Lei , Tang, Licheng , Fu, Chicheng , Fang, Qu , He, Chuan , Zhu, Xiaoli , Zhou, Xiaoyan and Bai, Qianming
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
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More information
Scope and Contents
Contents
Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy. This study aims to develop an automated method for quantifying HER2 fluorescence in situ hybridization (FISH) signals and improve the working efficiency of pathologists. An Aitrox artificial intelli...
Alternative Titles
Full title
Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images
Authors, Artists and Contributors
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_9ff49cb0f9bc4348bc974665868a0356
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_9ff49cb0f9bc4348bc974665868a0356
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-023-36811-z
