An adversarial learning approach to medical image synthesis for lesion detection

Liyan Sun, Jiexiang Wang, Yue Huang, Xinghao Ding, Hayit Greenspan, John Paisley

Research output: Contribution to journalArticlepeer-review

Abstract

The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly specialized knowledge. We propose a medical image synthesis model named abnormal-to-normal translation generative adversarial network (ANT-GAN) to generate a normal-looking medical image based on its abnormal-looking counterpart without the need for paired training data. Unlike typical GANs, whose aim is to generate realistic samples with variations, our more restrictive model aims at producing a normal-looking image corresponding to one containing lesions, and thus requires a special design. Being able to provide a 'normal' counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments. In the other aspect, the ANT-GAN model is also capable of producing highly realistic lesion-containing image corresponding to the healthy one, which shows the potential in data augmentation verified in our experiments.

Original languageEnglish
Article number8950113
Pages (from-to)2303-2314
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume24
Issue number8
DOIs
StatePublished - Aug 2020

Keywords

  • Medical image synthesis
  • generative adversarial network
  • unsupervised learning

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