Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary

by   Jean-Francois Rajotte, et al.

We introduce FELICIA (FEderated LearnIng with a CentralIzed Adversary) a generative mechanism enabling collaborative learning. In particular, we show how a data owner with limited and biased data could benefit from other data owners while keeping data from all the sources private. This is a common scenario in medical image analysis where privacy legislation prevents data from being shared outside local premises. FELICIA works for a large family of Generative Adversarial Networks (GAN) architectures including vanilla and conditional GANs as demonstrated in this work. We show that by using the FELICIA mechanism, a data owner with limited image samples can generate high-quality synthetic images with high utility while neither data owners has to provide access to its data. The sharing happens solely through a central discriminator that has access limited to synthetic data. Here, utility is defined as classification performance on a real test set. We demonstrate these benefits on several realistic healthcare scenarions using benchmark image datasets (MNIST, CIFAR-10) as well as on medical images for the task of skin lesion classification. With multiple experiments, we show that even in the worst cases, combining FELICIA with real data gracefully achieves performance on par with real data while most results significantly improves the utility.


page 7

page 8


Which Generative Adversarial Network Yields High-Quality Synthetic Medical Images: Investigation Using AMD Image Datasets

Deep learning has been proposed for the assessment and classification of...

GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification

Deep learning methods, and in particular convolutional neural networks (...

Private data sharing between decentralized users through the privGAN architecture

More data is almost always beneficial for analysis and machine learning ...

Backdoor Attack is A Devil in Federated GAN-based Medical Image Synthesis

Deep Learning-based image synthesis techniques have been applied in heal...

Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis

Deep Learning-based image synthesis techniques have been applied in heal...

GAN-based generative modelling for dermatological applications – comparative study

The lack of sufficiently large open medical databases is one of the bigg...

SkullGAN: Synthetic Skull CT Generation with Generative Adversarial Networks

Deep learning offers potential for various healthcare applications invol...

Please sign up or login with your details

Forgot password? Click here to reset