Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging
  Synthesis with Generative Adversarial Networks   Generative Adversarial Networks (GANs) have shown considerable promise for
mitigating the challenge of data scarcity when building machine learning-driven
analysis algorithms. Specifically, a number of studies have shown that
GAN-based image synthesis for data augmentation can aid in improving
classification accuracy in a number of medical image analysis tasks, such as
brain and liver image analysis. However, the efficacy of leveraging GANs for
tackling prostate cancer analysis has not been previously explored. Motivated
by this, in this study we introduce ProstateGAN, a GAN-based model for
synthesizing realistic prostate diffusion imaging data. More specifically, in
order to generate new diffusion imaging data corresponding to a particular
cancer grade (Gleason score), we propose a conditional deep convolutional GAN
architecture that takes Gleason scores into consideration during the training
process. Experimental results show that high-quality synthetic prostate
diffusion imaging data can be generated using the proposed ProstateGAN for
specified Gleason scores.

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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom