The polyp dataset involves the confidentiality of medical records, so it might be difficult to obtain datasets with accurate annotations. This problem can be effectively solved by expanding the polyp data set with algorithms. The traditional polyp dataset expansion scheme usually requires the use of two models or traditional visual methods. These methods are both tedious and difficult to provide new polyp features for training data. Therefore, our research aims to efficiently generate high-quality polyp samples, so as to effectively expand the polyp dataset. In this study, we first added the attention mechanism to the generation model and improved the loss function to reduce the interference caused by reflection in the image generation process. Meanwhile, we used the improved generation model to remove polyps from the original image. In addition, we used masks of different shapes generated by random combinations to generate polyps with more characteristic information. The same generation model was used for the removal and generation of polyps. The generated polyp image has its own annotation, which is conducive to us directly using the expanded data set for training. Finally, we verified the effectiveness of the improved model and the dataset expansion scheme through a series of comparative experiments on the public dataset. The results showed that using the dataset we generate for training can significantly optimize the main performance indicators.