Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology
James M. Dolezal(University of Chicago), Alexander T. Pearson(University of Chicago), Melanie C. Bois(Mayo Clinic in Arizona), Jefree J. Schulte(University of Chicago), Everett E. Vokes(University of Chicago Medical Center), Siddhi Ramesh(University of Chicago), Aaron S. Mansfield(Chinese University of Hong Kong), Brittany Cody(University of Chicago), Aliya N. Husain(University of Chicago), Aaron O. Bungum(Mayo Clinic), Radhika Bansal(Mayo Clinic in Arizona), Andrew Srisuwananukorn(University of Illinois Chicago), Marina Chiara Garassino(University of Chicago), Dmitry Karpeyev, Sagar Rakshit(Mayo Clinic in Arizona), Sara Kochanny(University of Chicago)
Cited by 123
Related Papers
First-Line Atezolizumab plus Chemotherapy in Extensive-Stage Small-Cell Lung Cancer
|New England Journal of Medicine|2018|3.5k
Osimertinib or Platinum–Pemetrexed in <i>EGFR</i> T790M–Positive Lung Cancer
|New England Journal of Medicine|2016|3.3k
Detection and localization of surgically resectable cancers with a multi-analyte blood test
|Science|2018|2.8k
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
|Nature Medicine|2019|1.4k