Synopsis

This talk introduces computational analytics towards speeding up scientific discovery and personalized diagnostics. It then focuses on two areas; a) radiogenomics, b) data-private collaborations. For radiogenomics, a hypothesis-driven study of the first imaging signature of EGFRvIII in glioblastoma, is presented, followed by data-driven studies on EGFRvIII and other EGFR mutations generating hypothesis for deeper investigations. Then, the first federated learning simulation in healthcare is discussed, followed by the first large-scale real-world federation in medicine, towards developing an AI model to detect glioblastoma boundaries based on 10,000 patients scans from >55 international collaborating sites, without sharing any patient data.