Federated learning: how the world's biggest federation is training state-of-the-art brain tumor segmentation models

Abstract

Federated learning is a paradigm that enables organizations to collaborate on machine learning projects without sharing sensitive patient data. This lowers the barrier to international collaboration to build generalizable models that mitigate bias by allowing access to larger and more diverse datasets. The talk will highlight key considerations in federated learning and discuss the results of the largest international federation of healthcare institutions that developed state-of-the-art brain tumor boundary detection model using MRI scans from 71 institutions across six continents.

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