Synthetic polyp generation is a good alternative to overcome the privacy problem of medical data and the lack of various polyp samples. In this study, we propose a deep learning-based polyp image generation framework that generates synthetic polyp images that are similar to real ones. We suggest a framework that converts a given polyp image into a negative image (image without a polyp) using a simple conditional GAN architecture and then converts the negative image into a new-looking polyp image using the same network. In addition, by using the controllable polyp masks, polyps with various characteristics can be generated from one input condition. The generated polyp images can be used directly as training images for polyp detection and segmentation without additional labeling. To quantitatively assess the quality of generated synthetic polyps, we use public polyp image and video datasets combined with the generated synthetic images to examine the performance improvement of several detection and segmentation models. Experimental results show that we obtain performance gains when the generated polyp images are added to the training set.

翻译：合成息肉生成是解决医学数据隐私问题及息肉样本类型不足的有效替代方案。本研究提出一种基于深度学习的息肉图像生成框架，可生成与真实息肉相似的合成息肉图像。我们提出的框架采用简单条件生成对抗网络架构，先将给定息肉图像转换为阴性图像（不含息肉的图像），再通过同一网络将该阴性图像转换为全新外观的息肉图像。此外，通过使用可控息肉掩膜，可从单一输入条件生成具有不同特征的息肉。生成的息肉图像可直接用作息肉检测与分割的训练图像，无需额外标注。为定量评估合成息肉的质量，我们将生成的合成图像与公开息肉图像及视频数据集结合，检验多种检测与分割模型的性能提升情况。实验结果表明，将生成的息肉图像加入训练集后，模型性能得到显著提升。