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Deep learning approach to predict tumor mutation burden (TMB) and delineate its spatial heterogeneity from whole slide images
Hongming Xu, Sunho Park, View ORCID ProfileJean René Clemenceau, Nathan Radakovich, Sung Hak Lee, Tae Hyun Hwang
doi: https://doi.org/10.1101/554527
Hongming Xu
1Department of Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, USA
Sunho Park
1Department of Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, USA
Jean René Clemenceau
1Department of Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, USA
Nathan Radakovich
1Department of Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, USA
Sung Hak Lee
2Department of Hospital Pathology, Seoul St.Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591 South Korea
Tae Hyun Hwang
1Department of Quantitative Health Sciences, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, USA
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Posted July 20, 2020.
Deep learning approach to predict tumor mutation burden (TMB) and delineate its spatial heterogeneity from whole slide images
Hongming Xu, Sunho Park, Jean René Clemenceau, Nathan Radakovich, Sung Hak Lee, Tae Hyun Hwang
bioRxiv 554527; doi: https://doi.org/10.1101/554527
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