Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation. (arXiv:2208.10553v1 [cs.CV])

Split learning (SL) has been proposed to train deep learning models in a
decentralized manner. For decentralized healthcare applications with vertical
data partitioning, SL can be beneficial as it allows institutes with
complementary features or images for a shared set of patients to jointly
develop more robust and generalizable models. In this work, we propose
"Split-U-Net" and successfully apply SL for collaborative biomedical image
segmentation. Nonetheless, SL requires the exchanging of intermediate
activation maps and gradients to allow training models …

brain data data leakage segmentation split learning