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)

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