Medical image synthesis for data augmentation and anonymization using generative adversarial networks

Hoo Chang Shin, Neil A. Tenenholtz, Jameson K. Rogers, Christopher G. Schwarz, Matthew L. Senjem, Jeffrey L. Gunter, Katherine Andriole, Mark Michalski

Research output: Contribution to journalArticlepeer-review


Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data. Together, these results offer a potential solution to two of the largest challenges facing machine learning in medical imaging, namely the small incidence of pathological findings, and the restrictions around sharing of patient data.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Jul 26 2018


  • Brain
  • Deep Learning
  • GAN
  • Generative Models
  • Image Synthesis
  • Magnetic Resonance Imaging
  • MRI
  • Segmentation
  • Tumor

ASJC Scopus subject areas

  • General

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