The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Frederick M. Howard(University of Chicago), Alexander T. Pearson(University of Chicago), Lara R. Heij(Maastricht University), Dezheng Huo(University of Chicago Medical Center), James M. Dolezal(University of Chicago), Sara Kochanny(University of Chicago), Robert L. Grossman(Memorial Sloan Kettering Cancer Center), Olufunmilayo I. Olopade(University of Chicago), Jefree J. Schulte(University of Chicago), Heather Chen(University of Chicago), Nicole A. Cipriani(University of Chicago), Jakob Nikolas Kather(Heidelberg University), Rita Nanda(University of Chicago)
Cited by 278
Related Papers
Toward a Shared Vision for Cancer Genomic Data
|New England Journal of Medicine|2016|1.8k
The molecular portraits of breast tumors are conserved across microarray platforms
|BMC Genomics|2006|1.5k
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
|Nature Medicine|2019|1.4k