Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
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The categories of this paper are: stat
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The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci