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Episode 19: Leveraging Federated Learning to Supplement an AI Learning Dataset
This episode discusses a study from UCLA in the United States that used federated learning to train a deep learning model for automatic segmentation and quantification of visceral and subcutaneous abdominal fat in children using free-breathing 3D MRI. By leveraging a larger adult dataset alongside a small pediatric cohort, the model achieved strong agreement with expert manual segmentation in under three seconds per patient.
Cross-cohort federated learning for pediatric abdominal adipose tissue segmentation and quantification using free-breathing 3D MRI. Zhang et al. Radiology Advances, 2026, 3, umag002
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By The Radiological Society of North AmericaThis episode discusses a study from UCLA in the United States that used federated learning to train a deep learning model for automatic segmentation and quantification of visceral and subcutaneous abdominal fat in children using free-breathing 3D MRI. By leveraging a larger adult dataset alongside a small pediatric cohort, the model achieved strong agreement with expert manual segmentation in under three seconds per patient.
Cross-cohort federated learning for pediatric abdominal adipose tissue segmentation and quantification using free-breathing 3D MRI. Zhang et al. Radiology Advances, 2026, 3, umag002