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Privacy-Preserved Federated Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans
2022
Frontiers in Communications and Networks
Semantic segmentation over three-dimensional (3D) intra-oral mesh scans (IOS) is an essential step in modern digital dentistry. Many existing methods usually rely on a limited number of labeled samples as annotating IOS scans is time consuming, while a large-scale dataset of IOS is not yet publicly available due to privacy and regulatory concerns. Moreover, the local data heterogeneity would cause serious performance degradation if we follow the conventional learning paradigms to train local
doi:10.3389/frcmn.2022.907388
fatcat:tthdemfz4fbzrcsg673jnyf6la