论文标题
自动息肉的生成对抗网络
Generative Adversarial Networks for Automatic Polyp Segmentation
论文作者
论文摘要
本文旨在使用生成的对抗网络框架为基础标记自动息肉分割问题做出贡献。将问题视为图像到图像翻译任务，有条件的生成对抗网络被用来生成由图像作为输入条件的掩模。发电机和歧视者都是基于卷积神经网络。该模型在Jaccard指数上达到0.4382，为0.611作为F2分数。
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.
