Nvidia uses federated learning to create medical imaging AI

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Nvidia uses federated learning to create medical imaging AI
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AI researchers from Nvidia and King’s College London have used federated learning to train a neural network for brain tumor segmentation, a milestone Nvidia claims is a first for medical image analysis.
The technique can allow data-sharing between hospitals and researchers while preserving patient privacy.
Federated learning is an approach to machine learning that — when using a client-server approach — can eliminate the need to create a single data lake in order to train models.
One, which we released last August, is create the best generalizable model that you have today and just send it to each one of these hospitals, where they can localize it for their own patients,” Nvidia director of healthcare Abdul Halabi told VentureBeat in a phone interview.
“The other one is to say: ‘Let’s fight together from the beginning, build this robust model [or generalizable model] as much as we can.’ And I think this research shows that it’s possible to actually do that here.
It’s possible for you to achieve a high-quality model without really bringing the data all together, which is why it’s really exciting.”
The model uses a data set from the BraTS (Multimodal Brain Tumor Segmentation) Challenge of 285 patients with brain tumors.
“But applying this was experimenting with real hospital data, using the BraTS challenge — to my knowledge, there’s no work out there that goes into the privacy direction.”
“So we’re saying that this research is really an important step toward the deployment of secure federated learning, and we hope that it will enable data-driven precision at [a] very large scale,” she said.
The work explores the limits of differential privacy, a technique to add noise to data sets in order to make federated learning models more secure.
Rieke said federated learning for medical image analysis comes with its own set of challenges, like the size of 3D medical images and the need for much more compute power

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