  One of the major obstacles in automatic polyp detection during colonoscopy is
the lack of labeled polyp training images. In this paper, we propose a
framework of conditional adversarial networks to increase the number of
training samples by generating synthetic polyp images. Using a normal binary
form of polyp mask which represents only the polyp position as an input
conditioned image, realistic polyp image generation is a difficult task in a
generative adversarial networks approach. We propose an edge filtering-based
combined input conditioned image to train our proposed networks. This enables
realistic polyp image generations while maintaining the original structures of
the colonoscopy image frames. More importantly, our proposed framework
generates synthetic polyp images from normal colonoscopy images which have the
advantage of being relatively easy to obtain. The network architecture is based
on the use of multiple dilated convolutions in each encoding part of our
generator network to consider large receptive fields and avoid many
contractions of a feature map size. An image resizing with convolution for
upsampling in the decoding layers is considered to prevent artifacts on
generated images. We show that the generated polyp images are not only
qualitatively realistic but also help to improve polyp detection performance.
