Machine Learning Effort Enables Large-Scale Cancer Study to Improve Boundary Detection
Scientists at the University of Pennsylvania School of Medicine and Intel Corp. carried out a large-scale international machine learning effort to collect knowledge from brain scans of more than 6,000 patients with glioblastoma at various sites globally. Their objective was to develop a model that could improve identification and prediction of boundaries in different tumor subcompartments. Spyridon Bakas, an assistant professor at Penn Medicine, stated that the study had the single biggest and most-diverse glioblastoma patient dataset ever considered in the literature, noting that this was facilitated through federated learning. Bakas also noted that the machine learning models became more…











