Federated Learning Enabling Big Data Analyses in Healthcare
Join Dr. Spyridon Bakas from the University of Pennsylvania’s Perelman School of Medicine as he shares insight following his study, “Federated learning (FL) enables big data for rare cancer boundary detection.” Dr. Bakas will discuss the results of the largest FL study to date that focuses on glioblastoma and leveraging the data of thousands of patients across six continents.
FL addresses concerns about reproducibility and generalizability of data from unseen sources. It approaches those concerns by training artificial intelligence models on data from collaborating sites while alleviating the need to share data at a centralized location. Developments in FL can pave the way for addressing clinical questions in rare diseases.
To view upcoming speakers or recordings of past presentations, visit the CBIIT Data Science Seminar Series webpage.
The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.
Dr. Bakas is an assistant professor at the University of Pennsylvania’s Perelman School of Medicine. He leads a research group that focuses on the development, application, and benchmarking of computational algorithms in medical imaging, with the intention of improving disease assessment and diagnosis in the current clinical practice.