ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks

TL;DR

This paper introduces ProstateGAN, a GAN-based model that synthesizes realistic prostate diffusion images conditioned on cancer grade, aiming to address data scarcity in prostate cancer analysis.

Contribution

ProstateGAN is the first GAN model tailored for generating prostate diffusion images conditioned on Gleason scores, enhancing data augmentation for prostate cancer research.

Findings

High-quality synthetic prostate images generated for specific Gleason scores

Improved data diversity for prostate cancer analysis

Potential to enhance classification accuracy with augmented data

Abstract

Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms. Specifically, a number of studies have shown that GAN-based image synthesis for data augmentation can aid in improving classification accuracy in a number of medical image analysis tasks, such as brain and liver image analysis. However, the efficacy of leveraging GANs for tackling prostate cancer analysis has not been previously explored. Motivated by this, in this study we introduce ProstateGAN, a GAN-based model for synthesizing realistic prostate diffusion imaging data. More specifically, in order to generate new diffusion imaging data corresponding to a particular cancer grade (Gleason score), we propose a conditional deep convolutional GAN architecture that takes Gleason scores into consideration during the training process. Experimental results show that high-quality synthetic prostate diffusion imaging data can be generated using the proposed ProstateGAN for specified Gleason scores.