A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from...
A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology
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Author / Creator
Zheng, Xueyi , Wang, Ruixuan , Zhang, Xinke , Sun, Yan , Zhang, Haohuan , Zhao, Zihan , Zheng, Yuanhang , Luo, Jing , Zhang, Jiangyu , Wu, Hongmei , Huang, Dan , Zhu, Wenbiao , Chen, Jianning , Cao, Qinghua , Zeng, Hong , Luo, Rongzhen , Li, Peng , Lan, Lilong , Yun, Jingping , Xie, Dan , Zheng, Wei-Shi , Luo, Junhang and Cai, Muyan
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
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Publication information
Publisher
London: Nature Publishing Group UK
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Scope and Contents
Contents
Epstein–Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting EB...
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Full title
A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_4a90617e65544ecfaff13ef323d48530
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4a90617e65544ecfaff13ef323d48530
Other Identifiers
ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-022-30459-5
