Unsupervised non‐small cell lung cancer tumor segmentation using cycled generative adversarial network with similarity‐based discriminator

The proposed smic-GAN performs comparably to the existing supervised methods UNet and Incre-MRRN. It does not rely on any manual annotations and can reduce the workload of training data preparation. It can also provide a good start for manual annotation in the training of supervised networks.