Abstract
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 across different feature spaces, which might leak data and raise privacy concerns. Therefore, we also quantify the amount of data leakage in common SL scenarios for biomedical image segmentation and provide ways to counteract such leakage by applying appropriate defense strategies.
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Notes
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- 2.
Implementation: We utilize components from MONAI (https://monai.io/) and NVIDIA FLARE (https://developer.nvidia.com/flare) to implement our SL simulation. In particular, we utilize MONAI’s BasicUNet as basis for Split-U-Net. All experiments were run on NVIDIA V100 GPUs with 16 GB memory.
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The current leading entry - Swin_UNETR [9] achieves an average Dice score of 0.647 for the three foreground tumor classes.
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Roth, H.R. et al. (2022). Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation. In: Albarqouni, S., et al. Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health. DeCaF FAIR 2022 2022. Lecture Notes in Computer Science, vol 13573. Springer, Cham. https://doi.org/10.1007/978-3-031-18523-6_5
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