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Generative Adversarial Networks (GANs) have shown considerable promise formitigating the challenge of data scarcity when building machine learning-drivenanalysis algorithms. Specifically, a number of studies have shown thatGAN-based image synthesis for data augmentation can aid in improvingclassification accuracy in a number of medical image analysis tasks, such asbrain and liver image analysis. However, the efficacy of leveraging GANs fortackling prostate cancer analysis has not been previously explored. Motivatedby this, in this study we introduce ProstateGAN, a GAN-based model forsynthesizing realistic prostate diffusion imaging data. More specifically, inorder to generate new diffusion imaging data corresponding to a particularcancer grade (Gleason score), we propose a conditional deep convolutional GANarchitecture that takes Gleason scores into consideration during the trainingprocess. Experimental results show that high-quality synthetic prostatediffusion imaging data can be generated using the proposed ProstateGAN forspecified Gleason scores.

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