FeTS 2022 Challenge
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About
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.
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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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