An Optimal Transport-Based Method For Medical Image Generation

Optimal transport (OT) is an effective technique for mapping between two distributions. Introducing a definition of distance between the distributions, OT provides the best calculable mapping plan, especially from an irregular distribution to a regular one. For this reason, OT has found wide applications, including deep learning, to mitigate some inherent risks in existing techniques. For example, generative adversarial networks (GANs) are widely used to generate medical images but sometimes experience mode collapse, a phenomenon that can be avoided by using OT. We use OT to map latent space distribution in a GAN model to a reasonable Gaussian distribution and generate more convincing and complete medical images to help train medical AI models. Our method consists of three steps. First, we train an auto-encoder as a basic structure of our model. Second, we introduce OT to the trained auto-encoder, which aligns the latent space distribution with a Gaussian distribution. This mapping helps to enforce a more realistic distribution of the generated images, enhancing their clinical relevance. Third, we construct a GAN-type model by training the decoder part of the auto-encoder and discriminator networks. We conducted a comprehensive experimental evaluation on a diverse medical dataset by comparing the performance of our proposed method to other state-of-the-art models. Results show that our method performs relatively better than other models by FID scores and provides more detailed information.