![AsyncGAN](http://rutgers.technologypublisher.com/files/sites/2020-101.jpg)
Learning without sharing medical images
![](data:image/png;base64,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)
Segmentation using synthetic images
Summary:
Large volumes of data are necessary to train machine learning (ML) algorithms (especially for medical image analysis.) HIPAA and other privacy regulations make it difficult to obtain sufficiently large volumes of disease related images (e.g., from different medical institutions) necessary for training machine learning models used in artificial intelligence (AI) based medical applications or medical analysis purpose.
Rutgers researchers have developed a data privacy-preserving and communication efficient distributed generative adversarial neural network (GAN) framework, referred to as Asynchronized Discriminator GAN (AsynDGAN), which resolves this problem by generating synthetic images for use in ML. The framework trains a central generator that creates the synthetic images by learning from one or more distributed discriminators. AsynDGAN can be used to generate (medical) images for training task-specific models without the need to access or store private patients’ data.
The framework has been validated on different datasets and demonstrated that a model trained solely by synthetic data has competitive performance with models trained by real data, and that it outperforms models trained locally, in each medical entity.
Advantages:
- A data privacy-preserving mechanism for access to sensitive data
- Adaptive to technology architecture updates
- Better communication efficiency and requires lower bandwidth
- Able to be used for non-medical applications
Market Applications: Any industry restricted by privacy rules and requiring enormous data sets in multiple physical locations for training ML algorithms, such as healthcare, automotive and financial industries.
Intellectual Property & Development Status: Patent Pending. Available for licensing and/or research collaboration.