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FeTS 2022 Challenge

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The FeTS 2022 Challenge focuses on benchmarking methods for federated learning, particularly targeting effective weight aggregation and algorithmic generalizability for brain tumor segmentation. With a background of over 8,000 clinically-acquired MRI scans, participants can compete in two tasks: Federated Training, focusing on weight aggregation methods, and Federated Evaluation, which seeks robust segmentation algorithms. The data is de-identified and linked to acquisition origins to facilitate federated learning. The challenge encourages collaborative biomedical research to advance treatments and technologies.

Platform
Web
Keywords
challengefederated learningcollaborative researchbrain tumor segmentation
Task
data segmentation
Features

• benchmarking methods for federated learning

• focus on cross-site generalization

• access to a large dataset of mri scans

• robust segmentation algorithms

• weight aggregation methods for federated training

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