Split- u - net:防止协同多模态脑肿瘤分割中分裂学习的数据泄漏

H. Roth, Ali Hatamizadeh, Ziyue Xu, Can Zhao, Wenqi Li, A. Myronenko, Daguang Xu
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引用次数: 5

摘要

Split learning (SL)被提出以分散的方式训练深度学习模型。对于具有垂直数据分区的分散医疗保健应用程序,SL可能是有益的,因为它允许具有互补功能或图像的机构为共享的一组患者共同开发更健壮和可泛化的模型。在这项工作中,我们提出了“Split-U-Net”,并成功地将SL应用于协同生物医学图像分割。尽管如此,SL需要交换中间激活映射和梯度,以允许跨不同特征空间的训练模型,这可能会泄露数据并引起隐私问题。因此,我们还量化了用于生物医学图像分割的常见SL场景中的数据泄漏量,并提供了通过应用适当的防御策略来抵消这种泄漏的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation
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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