Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation

@inproceedings{Roth2022SplitUNetPD,
  title={Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation},
  author={Holger R. Roth and Ali Hatamizadeh and Ziyue Xu and Can Zhao and Wenqi Li and Andriy Myronenko and Daguang Xu},
  booktitle={DeCaF/FAIR@MICCAI},
  year={2022}
}
. 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 insti-tutes 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… 
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