# Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information

arXiv: https://arxiv.org/abs/2012.08604 · HTML: https://mlsanity.com/paper/2012.08604/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Federated Split GANs](https://mlsanity.com/paper/2207.01750/) (2022)
- [Modality Bank: Learn multi-modality images across data centers without sharing medical data](https://mlsanity.com/paper/2201.08955/) (2022)

## Abstract (arXiv)

As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks. In the medical domain, however, large-scale and multi-parties data training and analyses are infeasible due to the privacy and data security concerns. In this paper, we propose an extendable and elastic learning framework to preserve privacy and security while enabling collaborative learning with efficient communication. The proposed framework is named distributed Asynchronized Discriminator Generative Adversarial Networks (AsynDGAN), which consists of a centralized generator and multiple distributed discriminators. The advantages of our proposed framework are five-fold: 1) the central generator could learn the real data distribution from multiple datasets implicitly without sharing the image data; 2) the framework is applicable for single-modality or multi-modality data; 3) the learned generator can be used to synthesize samples for down-stream learning tasks to achieve close-to-real performance as using actual samples collected from multiple data centers; 4) the synthetic samples can also be used to augment data or complete missing modalities for one single data center; 5) the learning process is more efficient and requires lower bandwidth than other distributed deep learning methods.