A synthetic dataset for polyp segmentation
We release CUT-seg, a new model that is computationally less expensive, and requires less real images than CycleGAN. It is composed by a segmentation model and a generative model that are jointly trained to produce realistic images while learning to segment polyps. We take advantage of recent one-sided translation models because they use significantly less memory, allowing us to add a segmentation model in the training loop.