Federated Multi-organ Segmentation with Partially Labeled Data. (arXiv:2206.07156v1 [eess.IV])

Web: http://arxiv.org/abs/2206.07156

Federated learning is an emerging paradigm allowing large-scale decentralized
learning without sharing data across different data owners, which helps address
the concern of data privacy in medical image analysis. However, the requirement
for label consistency across clients by the existing methods largely narrows
its application scope. In practice, each clinical site may only annotate
certain organs of interest with partial or no overlap with other sites.
Incorporating such partially labeled data into a unified federation is an
unexplored problem with …

arxiv data segmentation