论文标题
合成学习：从分布式异步歧视器GAN中学习而无需共享医疗图像数据
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
论文作者
论文摘要
在本文中，我们提出了一个数据隐私和通信有效的分布式GAN学习框架，称为分布式异步歧视器GAN（ASYNDGAN）。我们提出的框架旨在训练中央发电机从分布式歧视器中学习，并仅使用生成的合成图像来训练分割模型。我们验证了有关健康实体学习问题的拟议框架，该框架已知隐私敏感。我们的实验表明，我们的方法：1）可以从多个数据集中学习真实图像的分布，而无需共享患者的原始数据。 2）比其他分布式深度学习方法更有效，需要较低的带宽。 3）与一个由一个真实数据集训练的模型相比，与所有真实数据集训练的模型相比，实现了更高的性能。 4）可证明可以保证发电机可以以所有重要方式学习分布式分布，因此公正。
In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN). Our proposed framework aims to train a central generator learns from distributed discriminator, and use the generated synthetic image solely to train the segmentation model.We validate the proposed framework on the application of health entities learning problem which is known to be privacy sensitive. Our experiments show that our approach: 1) could learn the real image's distribution from multiple datasets without sharing the patient's raw data. 2) is more efficient and requires lower bandwidth than other distributed deep learning methods. 3) achieves higher performance compared to the model trained by one real dataset, and almost the same performance compared to the model trained by all real datasets. 4) has provable guarantees that the generator could learn the distributed distribution in an all important fashion thus is unbiased.
