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Synthetic polyp generation is a good alternative to overcome the privacyproblem of medical data and the lack of various polyp samples. In this study,we propose a deep learning-based polyp image generation framework thatgenerates synthetic polyp images that are similar to real ones. We suggest aframework that converts a given polyp image into a negative image (imagewithout a polyp) using a simple conditional GAN architecture and then convertsthe negative image into a new-looking polyp image using the same network. Inaddition, by using the controllable polyp masks, polyps with variouscharacteristics can be generated from one input condition. The generated polypimages can be used directly as training images for polyp detection andsegmentation without additional labeling. To quantitatively assess the qualityof generated synthetic polyps, we use public polyp image and video datasetscombined with the generated synthetic images to examine the performanceimprovement of several detection and segmentation models. Experimental resultsshow that we obtain performance gains when the generated polyp images are addedto the training set.

Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative and Negative to Polyp