High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks

Abstract

The ability to generate synthetic medical images is useful for data augmentation, domain transfer, and out-of-distribution detection. However, generating realistic, high-resolution medical images is challenging, particularly for Full Field Digital Mammograms (FFDM), due to the textural heterogeneity, fine structural details and specific tissue properties. In this paper, we explore the use of progressively trained generative adversarial networks (GANs) to synthesize mammograms, overcoming the underlying instabilities when training such adversarial models. This work is the first to show that generation of realistic synthetic medical images is feasible at up to 1280x1024 pixels, the highest resolution achieved for medical image synthesis, enabling visualizations within standard mammographic hanging protocols. We hope this work can serve as a useful guide and facilitate further research on GANs in the medical imaging domain.

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Cited by in corpus (9)

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• Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images
• Curriculum Learning for Deep Generative Models with Clustering
• Perceptual Image Super-Resolution with Progressive Adversarial Network
• Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images