Federated machine learning enables the largest brain tumor study to-date, without sharing patient data pennmedicine NatureComms
Credit: Unsplash/CC0 Public Domain
Researchers studying rare conditions, like GBM, an aggressive type of brain tumor, often have patient populations limited to their own institution or geographical location.
"Data helps to drive discovery, especially in rare cancers where available data can be scarce. The federated approach we outline allows for access to maximal data while lowering institutional burdens to data sharing." said Jill Barnholtz-Sloan, Ph.D., adjunct Professor at Case Western Reserve University School of Medicine.International Brain Tumor Segmentation challenge
This first the data of 231 patient cases from 16 sites, and the resulting model was validated against the local data at each site. The second stage, called theused the public initial model and incorporated more data from 2,471 patient cases from 35 sites, which improved its accuracy. The final stage, or, used the updated model, and incorporated the largest amount of data from 6,314 patient cases at 71 sites, across 6 continents, to further optimize and test for generalizability to unseen data.
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