A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics

Danh Tai Hoang*, Gal Dinstag, Eldad D. Shulman, Leandro C. Hermida, Doreen S. Ben-Zvi, Efrat Elis, Katherine Caley, Stephen John Sammut, Sanju Sinha, Neelam Sinha, Christopher H. Dampier, Chani Stossel, Tejas Patil, Arun Rajan, Wiem Lassoued, Julius Strauss, Shania Bailey, Clint Allen, Jason Redman, Tuvik BekerPeng Jiang, Talia Golan, Scott Wilkinson, Adam G. Sowalsky, Sharon R. Pine, Carlos Caldas, James L. Gulley, Kenneth Aldape, Ranit Aharonov, Eric A. Stone*, Eytan Ruppin*

*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Fingerprint

Dive into the research topics of 'A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics'. Together they form a unique fingerprint.

Keyphrases

Neuroscience

Biochemistry, Genetics and Molecular Biology

Immunology and Microbiology