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arXiv:1807.10225 (cs)
[Submitted on 26 Jul 2018 (v1), last revised 13 Sep 2018 (this version, v2)]

Title:Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks

Authors:Hoo-Chang Shin, Neil A Tenenholtz, Jameson K Rogers, Christopher G Schwarz, Matthew L Senjem, Jeffrey L Gunter, Katherine Andriole, Mark Michalski
View a PDF of the paper titled Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks, by Hoo-Chang Shin and 7 other authors
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Abstract: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.
Comments: Accepted for 2018 Workshop on Simulation and Synthesis in Medical Imaging - SASHIMI2018
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:1807.10225 [cs.CV]
  (or arXiv:1807.10225v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1807.10225
arXiv-issued DOI via DataCite

Submission history

From: Hoo Chang Shin [view email]
[v1] Thu, 26 Jul 2018 16:25:18 UTC (5,934 KB)
[v2] Thu, 13 Sep 2018 19:11:23 UTC (7,974 KB)
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Neil A. Tenenholtz
Jameson K. Rogers
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