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Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation. (arXiv:2208.10553v2 [cs.CV] UPDATED)
Sept. 28, 2022, 1:20 a.m. | Holger R. Roth, Ali Hatamizadeh, Ziyue Xu, Can Zhao, Wenqi Li, Andriy Myronenko, Daguang Xu
cs.CR updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.CR updates on arXiv.org
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