  This paper aims to contribute in bench-marking the automatic polyp
segmentation problem using generative adversarial networks framework.
Perceiving the problem as an image-to-image translation task, conditional
generative adversarial networks are utilized to generate masks conditioned by
the images as inputs. Both generator and discriminator are convolution neural
networks based. The model achieved 0.4382 on Jaccard index and 0.611 as F2
score.
