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.

翻译：本文的目的是利用基因对抗网络框架,协助确定自动聚合分割问题,将这一问题视为图像到图像的翻译任务,利用有条件的基因对抗网络生成以图像为输入条件的面具,产生者和歧视者都以神经网络演变为基础,模型在记分指数上达到0.4382,F2得分为0.611。