High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks

9 Jul 2018Dimitrios KorkinofTobias RijkenMichael O'NeillJoseph YearsleyHugh HarveyBen Glocker

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... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.