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 M. Redman, Tuvik Beker, Peng 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
Topics & Concepts
TranscriptomeArtificial intelligenceDeep learningComputer scienceDigital pathologyMedicineCancerMachine learningOncologyInternal medicineGene expressionBiologyGeneBiochemistryAI in cancer detectionCancer Genomics and DiagnosticsCell Image Analysis Techniques