Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
Thursday, May 28, 2020, 02:00pm - 03:30pm
Location : Remote via Webex
Prof. Dimitri Metaxas; Prof. Konstantinos Michmizos; Prof. Sungjin Ahn; Prof. Aaron Bernstein
Event Type: Qualifying Exam
Abstract: 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.