MammoGAN: High-Resolution Synthesis of Realistic Mammograms

Apr 17, 2019 MIDL 2019 Conference Abstract Submission readers: everyone Show Bibtex
  • Keywords: Image Synthesis, Generative Adversarial Networks, Breast Mammography
  • TL;DR: We explore whether recent advances in generative adversarial networks (GANs) enable synthesis of realistic medical images that are hard to distinguish from real ones, even by domain experts.
  • Abstract: We explore whether recent advances in generative adversarial networks (GANs) enable synthesis of realistic medical images that are hard to distinguish from real ones, even by domain experts. High-quality synthetic images can be useful for data augmentation, domain transfer, and out-of-distribution detection. However, generating realistic images is challenging, particularly for Full Field Digital Mammograms (FFDM), due to the high-resolution, textural heterogeneity, fine structural details and specific tissue properties. We employ progressive GANs to synthesize mammograms at a resolution of 1280x1024 pixels, the highest reported so far. In order to assess the perceptual realism, we designed a user study where experts are asked to distinguish real and generated images with exciting results.
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