Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Red blood cell image generation for data augmentation using Conditional
  Generative Adversarial Networks   In this paper, we describe how to apply image-to-image translation techniques
to medical blood smear data to generate new data samples and meaningfully
increase small datasets. Specifically, given the segmentation mask of the
microscopy image, we are able to generate photorealistic images of blood cells
which are further used alongside real data during the network training for
segmentation and object detection tasks. This image data generation approach is
based on conditional generative adversarial networks which have proven
capabilities to high-quality image synthesis. In addition to synthesizing blood
images, we synthesize segmentation mask as well which leads to a diverse
variety of generated samples. The effectiveness of the technique is thoroughly
analyzed and quantified through a number of experiments on a manually collected
and annotated dataset of blood smear taken under a microscope.

28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
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The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat