Synthetic data augmentation using GAN for improved liver lesion classification

In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.

• Publication year

  2018

• Venue

  IEEE International Symposium on Biomedical Imaging

• Publication date

  2018-01-08

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1109/ISBI.2018.8363576arXiv 1801.02385
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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